Eco-Speed Control for Hybrid Electric Buses in the Vicinity of Signalized Intersections Date: February 2019 Hao Chen, Hesham Rakha and Ihab El-Shawarby, PhD, VTTI Mansoureh Jeihani and Celeste Chavis, PhD, MSU Kyungwon Kang, Graduate Student, VTTI Samira Ahangari and Zohreh Rashidi Moghaddam, Graduate Student, MSU Prepared by: Virginia Tech Transportation Institute 3500 Transportation Research Plaza Blacksburg, VA 24061 Department of Transportation and Urban Infrastructure Studies Morgan State University 1700 E. Cold Spring Lane, Baltimore, MD 21251 Prepared for: Mid-Atlantic Transportation Sustainability University Transportation Center University of Virginia Charlottesville, VA 22904

FINAL REPORT

1. Report No.

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle Eco-Speed Control for Hybrid Electric Buses in the Vicinity of Signalized Intersections

5. Report Date February 2019

6. Performing Organization Code

7. Author(s) Hao Chen, Hesham Rakha, Ihab El-Shawarby, Mansoureh Jeihani, Celeste Chavis, Kyungwon Kang, Samira Ahangari, and Zohreh Rashidi Moghaddam.

8. Performing Organization Report No.

9. Performing Organization Name and Address Virginia Tech Blacksburg, VA 24061

10. Work Unit No. (TRAIS

11. Contract or Grant No. DTRT13-G-UTC33

12. Sponsoring Agency Name and Address US Department of Transportation Office of the Secretary-Research UTC Program, RDT-30 1200 New Jersey Ave., SE Washington, DC 20590

13. Type of Report and Period Covered Final 05/01/16 – 12/31/18

14. Sponsoring Agency Code

15. Supplementary Notes

16. Abstract This study develops, implements and field tests a bus eco-driving system from a previously developed light duty vehicle (LDV) Eco-CACC system. In the proposed bus Eco-CACC system, a simple and easy calibrated bus fuel consumption model is used in a moving-horizon dynamic programming framework to compute the fuel-optimum bus trajectory in the vicinity of signalized intersections. The proposed bus Eco-CACC system was implemented in a diesel bus provided by Blacksburg Transit and field tested using 30 participants. The field test attempted to investigate transit vehicle performance under the combination of different scenarios. The test results demonstrated the benefits of the proposed bus Eco-CACC system in assisting the bus to drive smoothly in the vicinity of signalized intersections and therefore reduce fuel consumption and vehicle delay. In addition, this study utilizes a full-scale high-fidelity driving simulator to investigate drivers’ response and compliance to Eco-speed control systems in the vicinity of a signalized intersection, and the effectiveness of such a system in reducing emissions. An Eco-driving guidance is implemented in the driving simulator. Descriptive and statistical analyses including Generalized Linear Models (GLM) and t-tests are performed on the data obtained from 58 participants. The results show that men and younger drivers are more likely to follow the recommended speed. The emission calculations indicate that an Eco-speed control system decreases the emission level 9.1% more than countdown timing systems do and the emission level is lower in the countdown timing system compared to conventional traffic signals.

17. Key Words Eco-driving, Bus, Eco-Cooperative Adaptive Cruise Control, Signalized intersection, Fuel consumption model, Field test, driver behavior, driving simulator.

18. Distribution Statement No restrictions. This document is available from the National Technical Information Service, Springfield, VA 22161

19. Security Classif. (of this report) Unclassified

20. Security Classif. (of this page) Unclassified

21. No. of Pages XX

22. Price

ACKNOWLEDGMENTS

This project was funded by Mid-Atlantic Transportation Sustainability University Transportation Center (MATS UTC). The authors acknowledge the support from the Blacksburg Transit (BT) by providing buses and drivers for the field test. The authors also acknowledge the help from the Center for Technology Development at the Virginia Tech Transportation Institute (VTTI) in developing the hardware and software environment for the field test.

DISCLAIMER

The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation’s University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof.

TABLE OF CONTENTS Problem ........................................................................................................................................... 1Approach ......................................................................................................................................... 2Methodology ................................................................................................................................... 2Findings .......................................................................................................................................... 3Conclusions ..................................................................................................................................... 4Recommendations ........................................................................................................................... 4Complete Documentations .............................................................................................................. 6

Literature Review ........................................................................................................................ 6Develop Bus Eco-CACC System ................................................................................................ 8

Eco-CACC for Buses .............................................................................................................. 8Bus Fuel Consumption Model .............................................................................................. 10

Field Test of Bus Eco-CACC System ....................................................................................... 11Test Environment .................................................................................................................. 11Experimental Design and Statistical Analysis ...................................................................... 15Quantitative Performance Analysis ...................................................................................... 17

Driving Simulator Test ............................................................................................................. 21Test Environment .................................................................................................................. 21Data Collection ..................................................................................................................... 23

Test Result and Discussion ....................................................................................................... 24Following recommended speed analysis .............................................................................. 24Calculating CO2 emissions ................................................................................................... 25Regression Analysis .............................................................................................................. 26Compliance Model ................................................................................................................ 27

References ..................................................................................................................................... 30

LIST OF FIGURES FIGURE 1 Optimum speed profile in case 2. ................................................................................. 9FIGURE 2 Model validation for bus fuel consumption model. .................................................... 11FIGURE 3 Layout of the Test Road (Source: Google Maps). ..................................................... 12FIGURE 4 Hardware of vehicle onboard units in the bus Eco-CACC system. .......................... 14FIGURE 5 Structure of the split-split-plot design. ...................................................................... 15FIGURE 6 Compare test results for fuel levels and travel times. ................................................ 19FIGURE 7 Vehicle speed profiles of a selected participant for downhill direction under various red offset timings: (a) 10 seconds; (b) 15 seconds; (c) 20 seconds; (d) 25 seconds. .................... 20FIGURE 8 Vehicle speed profiles of a selected participant for uphill direction under various red offset timings: (a) 10 seconds; (b) 15 seconds; (c) 20 seconds; (d) 25 seconds. .......................... 20FIGURE 9 Driving Simulator. ..................................................................................................... 22FIGURE 10 Snapshot of driving simulator environment. ........................................................... 22

LIST OF TABLES TABLE 1 Results of Fixed Effect Tests. ...................................................................................... 16TABLE 2 Average Trip Fuel Consumption (FC) Levels. ............................................................ 17TABLE 3 Average Trip Travel Times. ......................................................................................... 18TABLE 4 Scenario descriptions .................................................................................................. 23TABLE 5 Participants' Socioeconomic Characteristics ............................................................... 24TABLE 6 ANOVA Results for following speed .......................................................................... 25TABLE 7 Tukey’s Results for following speed ........................................................................... 25TABLE 8 ANOVA analysis of CO2 Emissions by Scenarios ...................................................... 25TABLE 9 Tukey's Post Hoc Analysis of Emissions in different Scenarios ................................. 26TABLE 10 Regression Results for Uphill Scenarios ................................................................... 26TABLE 11 Regression Results for Downhill Scenarios .............................................................. 27TABLE 12 Compliance Model .................................................................................................... 28

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Problem Studies have shown that vehicle fuel consumption levels in the vicinity of signalized intersections are dramatically increased due to vehicle deceleration and acceleration maneuvers (Barth & Boriboonsomsin, 2008; H. Rakha, Ahn, & Trani, 2003). Meanwhile, knowledge of traffic signal phase and timing (SPaT) has been proven to benefit the energy use of vehicles by reducing the stop-and-go maneuver and idling time at signalized intersections (Mahler & Vahidi, 2012). With the development of information and communication technology, the advanced communication power in connected vehicle (CV) environment ensures a very high update rate of information can be provided to vehicles. For example, SPaT information, vehicle speed, surrounding vehicle location can be shared using vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. Such information enables researchers to develop connected transportation systems meeting safety, economy, and efficiency challenges. Recently, researchers have attempted to use connected vehicles and infrastructure technologies to develop eco-driving strategies to provide, in real-time, recommendations to individual drivers or vehicles so that vehicle maneuvers can be adjusted accordingly to reduce fuel consumption and emission levels in the vicinity of signalized intersections (Barth & Boriboonsomsin, 2009; Saboohi & Farzaneh, 2008, 2009).

Various eco-driving algorithms were developed by researchers in recent years. Malakorn and Park proposed a cooperative adaptive cruise control system using SPaT information to minimize absolute acceleration levels of vehicles and reduce vehicle fuel consumption levels (Malakorn & Park, 2010). Kamalanathsharma and Rakha developed a dynamic programming based fuel-optimization strategy using recursive path-finding principles, and evaluated the developed strategy using an agent-based modeling approach (R. Kamalanathsharma & Rakha, 2014). Asadi and Vahidi proposed a schedule optimization algorithm to allocate “green-windows” for vehicles to pass through a series of consecutive signalized intersections (Asadi & Vahidi, 2011). Guan and Frey further extended the work in (Asadi & Vahidi, 2011) to generate a brake-specific fuel consumption map which enables optimization of gear ratios, and dynamic programming is used to find the optimum solution (Guan & Frey, 2013).

However, the studies in this area are mainly focused on developing eco-driving strategies for LDVs. Compared to LDVs, heavy duty vehicles (HDVs) such as buses have poor fuel efficiency, especially in stop-and-go traffic conditions, due to their large curb weights and sizes. Considering that energy consumption model is an important component to compute the optimum control solution in eco-driving, the main difficulty in designing eco-driving systems for buses is that the energy consumption models for buses are hard to develop and calibrate. A few studies attempted to develop eco-driving systems to save fuel and emission when buses pass signalized intersections. Zhang et al. proposed a bus eco-driving system by adjusting vehicle speed profile and the dwell time at bus stations to ensure that buses can smoothly pass downstream signalized intersections (Zhang, Liang, & Zheng, 2018) . A MATLAB simulated environment was used to validate the benefit of the proposed system and showed a saving of 5.5% emissions. Another similar approach was developed by Bagherian et al. to minimize the number of bus stops at intersections to reduce the amount of transit fuel consumption in urban areas (Bagherian, Mesbah, & Ferreira, 2016). According to the predicted bus arrival time to the upcoming intersection and the corresponding signal timings, the bus speed and the dwell time were adjusted so that the bus can drive smoothly to approach intersection. The saving of fuel consumption was achieved by moving vehicle completely stops at signalized intersections to bus stations, thus reducing the total number of stops and removing accelerations and decelerations at intersections. The proposed method was implemented into VISSIM microsimulation software

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and the test results presented up to 15% savings on bus fuel consumption at intersections. Both studies tried to reduce buse stopping at intersections by adjust bus dwell time at upstream stations. However, these approaches may not work well for signalized intersections without or far away from neighboring bus stations.

Moreover, the developed eco-driving algorithms have been primarily tested in traffic simulations that make numerous simplified assumptions that deem them unrealistic. For instance, simulations typically assume that drivers can accurately and instantly follow the speed advisories, eco-driving systems run perfectly without providing erroneous information, latencies and loss of data in communication are neglected, traffic signal timing information are known precisely, etc. Consequently, field tests are very important to explore the benefits of eco-driving systems on real roads. Recently, the Virginia Tech Transportation Institute developed an eco-driving system entitled Eco-Cooperative Adaptive Cruise Control (Eco-CACC) that includes two modes of operation, namely; a manual Eco-CACC for CVs and an automated Eco-CACC for connected-automated vehicles (CAVs) (Almannaa, Chen, Rakha, Loulizi, & El-Shawarby, 2017; Chen, Rakha, Almannaa, Loulizi, & El-Shawarby, 2017; H. A. Rakha et al., 2016). Drivers follow recommended speed advisories that are provided via audio alerts in the manual Eco-CACC system. Alternatively, CAVs vehicles use longitudinally automated control to follow the optimum speed profile that is computed by the Eco-CACC system. The field tests indicated the manual and automated Eco-CACC systems produce fuel savings of 28% and 38% on average, respectively. A similar eco-driving system called GlidePath was developed and tested at the Turner Fairbank Highway Research Center, which also can be used for CVs and CAVs (Qi, Barth, Wu, Boriboonsomsin, & Wang, 2018). A few more similar eco-driving systems were developed in other countries such as the Green Light Optimal Speed Advisory (GLOSA) System in Europe (Katsaros, Kernchen, Dianati, & Rieck, 2011). However, those studies only used LDVs to design and test the eco-driving systems, without the consideration of HDVs such as buses.

Approach In order to solve the abovementioned problems, this study expand upon our previous research efforts to develop and test a dynamic ESC system that computes the optimum vehicle trajectories of ICE light duty vehicles and buses in the vicinity of signalized intersections while considering dynamic traffic conditions. As part of this effort, we developed ESC algorithms for light duty vehicles and buses using predictive energy estimation models to identify the optimum speed profiles using information from surrounding vehicles and upcoming signalized intersections. Field tests of these systems were conducted on the Smart Road test facility at VTTI. Furthermore, we also implemented and tested the proposed ESC algorithms on a driving simulator under different conditions (e.g., roadway speed limit, time to the next signal phase, and vehicle type) at MSU.

Methodology Based on our previous research on developing eco-driving strategies for light duty gasoline vehicles, the bus eco-driving strategy is modeled as an optimization problem by using vehicle dynamic model, fuel consumption model, vehicle trajectory and traffic signal information. In order to solve the optimization problem in the proposed bus Eco-CACC system, the bus fuel consumption model is a key component to calculate and compare the trip fuel consumption level for the speed profile in each possible solution. The diesel bus fuel consumption model developed in [18, 19] was selected to use here by considering two reasons: 1) this fuel model only needs

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instantaneous speed data as input to compute fuel consumption; 2) the model calibrate is very easy without the need of vehicle power or engine data. The details of the bus Eco-CACC system and fuel model are described in the complete documentation section. In addition, the experiment design, data preparation and analysis for the bus field test at VTTI and driving simulator test at MSU are detailed described in the complete documentation section.

Findings This study is the first effort to develop and field test bus eco-driving system to pass signalized intersections using V2I and V2V information under connected vehicle environment. The test results demonstrated the benefits of the proposed bus Eco-CACC system in assisting the bus to drive smoothly in the vicinity of signalized intersections and therefore reduce bus fuel consumption levels and delays. Compared to the uninformed drive scenario, the test results demonstrated that the proposed bus Eco-CACC system can efficiently reduce fuel levels (22.1%) while also producing travel time savings (6.1%).

This study also is one of a kind to test the ability of drivers to follow and comply with the ESC recommended speed using a full-scale high-fidelity driving simulator. The effect of sociodemographic characteristics of drivers in their compliance was also investigated. Furthermore, we evaluated the effects of the ESC system and countdown timing on emissions reduction in the vicinity of a signalized intersection.

A sample of 58 participants took part in driving simulator experiments, driving a total of 2,000 times in different scenarios of speed guidance (no guidance, voice-activated ESC speed guidance every two seconds, and countdown signal timing), traffic regimes (no traffic and mild traffic), and road characteristics (uphill, downhill and with one lane or three lanes in each direction). Age appeared to be a significant factor; younger participants followed the speed recommendations 9.6% more than did participants in the older age groups. To the best knowledge of the authors, this is the first study to investigate the effect of sociodemographic factors on ESC compliance, which would help researchers and the auto industry to consider more effective ESC systems among different sociodemographic groups. Less frequent speed guidance might reduce distraction and increase compliance among older drivers, which should be tested in the future. Unlike most studies which used a display system for speed guidance, this study implemented voice command to reduce drivers’ distractions. To the best knowledge of the authors, this is the first study to use voice command for ESC system. Furthermore, we found that drivers’ socioeconomic characteristics such as age, annual income, and household size, as well as their driving behavior (average speed and start time of following the recommended speed) affect ESC compliance. Our results are in line with most studies in the literature; speed and driver behavior affect the success of ESC. Our study also concludes that sociodemographic characteristics play a role in ESC’s success. As expected, the probability of following ESC is higher in drivers in higher income categories than others. The results show that older drives were less successful in following the ESC. Therefore, researchers and car manufacturers should come up with better ways to disseminate the information so that it is more easily followed by older drivers.

The results related to emissions show that the ESC system reduced emissions by 10.3% for the whole trip. We did not find a significant change in emissions due to countdown timing. The results indicate that using an ESC system is fairly effective in emissions reduction and about half of the participants could comply with the recommended speed. The compliance would increase over time as drivers get used to it. About 89% of participants stated in the survey that speed advisory is useful, and about 76% of participants stated that following the recommended speed is easy for them.

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This study did not test the autonomous version of the ESC, which would increase compliance. The authors suggest implementing such systems in vehicles with autonomous capabilities, in which drivers could give the vehicle’s control to the system to pass through the intersection without stopping at red lights. Future study will consider several intersections with coordinated traffic lights to find the effectiveness of ESC in emissions reduction. Comparing different types of speed guidance dissemination, such as display and voice, also warrants additional study.

Conclusions This study develops, implements and field tests a bus eco-driving system from a previously developed light duty vehicle (LDV) Eco-CACC system. In the proposed bus Eco-CACC system, a simple and easy calibrated bus fuel consumption model is used in a moving-horizon dynamic programming framework to compute the fuel-optimum bus trajectory in the vicinity of signalized intersections. The computation speed is expedited using an A-star algorithm. The proposed bus Eco-CACC system was implemented in a diesel bus provided by Blacksburg Transit. The Virginia Smart Road test facility was used to conduct the field test using 30 participants. Each participant drove three scenarios including a base case uninformed drive, an informed drive with signal timing, and an informed drive with the recommended speed computed by the Eco-CACC system. The field test attempted to investigate transit vehicle performance under the combination of different scenarios, including: road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated the benefits of the proposed bus Eco-CACC system in assisting the bus to drive smoothly in the vicinity of signalized intersections and therefore reduce fuel consumption and vehicle delay. Compared to the uninformed drive, the bus Eco-CACC system resulted in average fuel and travel time savings of 22.1% and 6.1%, respectively.

This study also utilizes a full-scale high-fidelity driving simulator to investigate drivers’ response and compliance to Eco-speed control systems in the vicinity of a signalized intersection, and the effectiveness of such a system in reducing emissions. An Eco-driving guidance is implemented in a driving simulator, and participants are given a voice-activated recommended speed every two seconds while driving toward an intersection in different scenarios of traffic conditions and road characteristics. Their speed profiles and the produced CO2 emissions are compared with driving the same road without any guidance and also with a countdown traffic signal. Descriptive and statistical analyses including Generalized Linear Models (GLM), ANOVA, and post hoc Tukey are performed on the data obtained from 58 participants with various sociodemographic backgrounds who in total drove more than 2,000 times. The results show that younger drivers are more likely to follow the recommended speed. The emissions calculations indicate that an Eco-speed control system decreases the emissions level 10.3% compared to no guidance, and the emissions level is not significantly lower in the countdown timing system compared to conventional traffic signals.

Recommendations Based on the study findings, the recommendations are listed as below.

1. In this study, the field test demonstrates that bus drivers can control bus to pass signalized intersections with less fuel by following the speed recommendations from the proposed system. In the future research, it’s recommended to implement the proposed system into automated buses to test if the fuel consumption can be further reduced. In addition, the bus Eco-CACC system is recommended to be tested within a microscopic simulation

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environment to validate the network level performances under various traffic conditions and heterogeneous traffic including LDVs and buses

2. This study did not test the autonomous version of the ESC, which would increase compliance. The authors suggest implementing such systems in vehicles with autonomous capabilities, in which drivers could give the vehicle’s control to the system to pass through the intersection without stopping at red lights. Future study will consider several intersections with coordinated traffic lights to find the effectiveness of ESC in emissions reduction. Comparing different types of speed guidance dissemination, such as display and voice, also warrants additional study.

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Complete Documentations This section provides a detailed overview of all the work undertaken in this project including literature synthesis, data collection process, data description, mathematical formulation of models, experiment design, test data result and analysis.

Literature Review Studies have shown that vehicle energy consumption levels in the vicinity of signalized intersections dramatically increased as a result of vehicles decelerating and accelerating. Researchers have attempted to use connected vehicle and infrastructure technologies to develop eco-driving strategies to improve fuel efficiency. One such application is Eco-Speed Control (ESC), which attempts to optimize individual vehicle fuel consumption levels by recommending fuel-efficient trajectories through intersections based on communication with surrounding vehicles and upcoming signalized intersections.

Several ESC systems have been developed (Kamalanathsharma & Rakha, 2013; Ahn, Rakha, & Moran, 2011; H. Rakha & Kamalanathsharma, 2011; Kamal, Mukai, Murata, & Kawabe, 2013). However, the effectiveness of these systems in emissions reduction for different traffic regimes and various sociodemographic groups has not been fully investigated. To the authors’ best knowledge, no study tested ESC systems in different traffic regimes. Also, the samples tested in all existing studies are very small. Furthermore, the ability of drivers to follow and comply with the recommended speed when these systems are not fully automated has not been tested comprehensively. This study implements the ESC system developed by (Chen, Rakha, Almannaa, Loulizi, & El-Shawarby, 2017; Chen, Rakha, Loulizi, El-Shawarby, & Almannaa, 2016) in a full-scale high-fidelity 3D driving simulator to fill these gaps. We investigate the effect of both ESC and countdown signal timing on emissions reduction using a driving simulator. Over 2,000 experiments in various traffic regimes and road characteristics (one-lane and three-lane road, and uphill and downhill) using 58 participants from a diverse sociodemographic background are generated to investigate drivers’ compliance behavior and emissions reduction. The purpose of the study is to find whether participants are able to follow the recommended speed, what sociodemographic groups are able to follow it better, and if following the recommended speed will decrease CO2 emissions.

In recent years, due to increases in the level of vehicle energy consumption and air pollutant emissions, reducing transportation-related fuel consumption necessitates urgent attention. Researchers attempted to use connected vehicle and infrastructure technologies to advance eco-driving strategies to improve fuel efficiency, as studies show eco-driving is effective at improving the fuel efficiency of the transportation arena (Jollands et al., 2010). Eco-driving provides real-time driving guidance to individual vehicles so that the drivers can adjust their driving behavior to decrease fuel consumption and emissions levels (Ando & Nishihori, 2011). Usually, eco-driving strategies operate by providing real-time driving guidance such as speed alerts. In this regard, most research efforts focus on controlling speed to decrease vehicle energy consumption, especially in the vicinity of a signalized intersection, since traffic signals typically cause vehicles to stop. The resulting acceleration and deceleration increase vehicle fuel consumption (MERKiSz, Andrzejewski, & PiELECHA, 2013).

Providing information for drivers regarding their speed and traffic signal changes would help control speed, which in turn would decrease fuel consumption. The provision of information in the signalized intersection would help the driver pass through the intersection with a consistent speed instead of rapid acceleration and deceleration. Some studies examined driver’s speed behavior (Andrieu & Pierre, 2012; Rolim, Baptista, Duarte, Farias, & Shiftan, 2014; af W\a

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ahlberg, 2007) with the information provided regarding the signalized intersection (Kamal, Taguchi, & Yoshimura, 2015). For example, Matsumoto & Peng (2015) provided different information regarding the speed with which drivers passed through a signalized intersection while following the recommended speed using the driving simulator. Eco-driving was developed by different researchers to assist drivers with the recommended speed information as it relates to the traffic signal (Mandava, Boriboonsomsin, & Barth, 2009; Xia, Boriboonsomsin, & Barth, 2013; Asadi & Vahidi, 2011), and some studies found factors that make Eco-driving more effective (Ando & Nishihori, 2012). Also, in 2016 Zhou, Jin, & Wang reviewed fuel consumption models and presented the travel-related issues that affect fuel economies such as weather, vehicle, roadway, traffic, and driver. Yang, Rakha, & Ala (2017) concluded that the eco-cooperative adaptive cruise control (Eco-CACC) system that receives signal phasing and timing data from downstream signalized intersections results in vehicle fuel savings of up to 40%. The outcomes of the study by Asadi & Vahidi (2011) showed that vehicles equipped with a Speed Advisory System (SAS) advance not only their fuel economy but also help other conventional vehicles. In 2015 Matsumoto & Peng concluded that information provision significantly reduced unnecessary vehicle movements. They also found that the information provision reduced the amount of CO2 emissions by 6%. The energy usage efficiency could be increased up to 19% by controlling the vehicle speed and accelerator pedal stroke (Younsun Kim, Ingeol Lee, & Sungho Kang, 2015; Hu, Shao, Sun, & Bared, 2017; Ala, Yang, & Rakha, 2016). Also, Zheng & Zhang (2015) formulated an optimal control of bus transit using Vehicle-to-Infrastructure (V2I) communication to minimize the volume of vehicle emissions.

Several researchers studied the countdown device that displays the remaining green time or waiting red time in order to investigate intersection efficiency in the presence of countdown information (Limanond, Prabjabok, & Tippayawong, 2010; Lum & Halim, 2006; Wenbo, Zhaocheng, Xi, & Feifei, 2013; Fujita, Suzuki, & Yilmaz, 2007). They concluded that the information provided via a countdown system helps the drivers make a decision rather than hesitating because they didn’t know when the light would change. Because the countdown timer offers drivers information about the onset of the next phase, the drivers can make a better decision about responding to the upcoming change, or utilizing the time waiting for the onset of the green phase. Chiou & Chang (2010) concluded that the red signal countdown display (RSCD) improves intersection efficiency and RSCD is more helpful than the green signal countdown display (GSCD). According to the result of the analysis by Yu, Fujita, & Suzuki (2011), countdown traffic signals shorten start-up delay time. Traffic light control plays a prominent role in reducing vehicle waiting time, and constant-speed driving would lead to lower CO2 emissions (Barth & Boriboonsomsin, 2009; Li & Shimamoto, 2012; Li & Shimamoto, 2011). In 2004 Oda, Kuwahara, & Niikura used a driving simulator to find how much controlling the traffic signal decreased emissions. They found a 7% reduction of CO2 emissions by the traffic signal control method. In order to control CO2 emissions, a real-time traffic light control technique was used via increasing the green light timing at the junction (Li & Shimamoto, 2012; Telang & Terdal, n.d.).

Several researchers implemented different types of information guidance such as display message, voice command, and in-vehicle assistance in driving simulators. Most studies provided speed guidance via display message (Daun, Braun, Frank, Haug, & Lienkamp, 2013; Niu & Sun, 2013; Y. Zhao et al., 2016; Kircher, Fors, & Ahlstrom, 2014; Staubach, Schebitz, Köster, & Kuck, 2014; Matsumoto & Peng, 2015; Fors, Kircher, & Ahlström, 2015). Very few studies provided speed guidance using voice command (X. Zhao, Wu, Rong, & Zhang, 2015). In-vehicle guidance and other types of eco-driving guidance were used by some other researchers (Pampel, Jamson, Hibberd, & Barnard, 2015; A. Hamish Jamson, Hibberd, & Merat, 2015; A. H. Jamson,

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Hibberd, & Merat, 2013; Azzi, Reymond, Mérienne, & Kemeny, 2011). The previous studies verified that providing speed guidance reduces emissions, but such guidance – especially reading an eco-driving message – could potentially distract the driver (Rouzikhah, King, & Rakotonirainy, 2013). Zhao et al. (42) provided voice command in the form of non-eco-driving behavior like “Please avoid accelerating rapidly” and instant CO2 emissions to support eco-driving training using a driving simulator. This study provides voice-activated guidance in the form of an exact target speed to analyze drivers’ behavior related to following the recommended speed. For example, the message states 38 when the target speed is 38 miles per hour.

Develop Bus Eco-CACC System We modified the Eco-CACC system previously developed for LDVs to use for buses. More details of the Eco-CACC system for LDVs can be found in (Chen et al., 2017; H. A. Rakha et al., 2016). In order to solve the optimization problem in the proposed bus Eco-CACC system, the bus fuel consumption model is a key component to calculate and compare the trip fuel consumption level for the speed profile in each possible solution. The diesel bus fuel consumption model developed in (Wang & Rakha, 2016, 2017) was selected to use here by considering two reasons: 1) this fuel model only needs instantaneous speed data as input to compute fuel consumption; 2) the model calibrate is very easy without the need of vehicle power or engine data. The Eco-CACC system and fuel model for buses are described as below. Eco-CACC for Buses Considering the communication range of dedicated short range communication (DSRC) systems, the Eco-CACC is activated at a distance of dup upstream of the intersection to a distance of ddown downstream of the intersection. Note that the distance is calculated from the vehicle location to the intersection stop line. The value of ddown is defined to ensure that the vehicle has enough downstream distance to accelerate from zero speed to the speed limit at a low throttle level (e.g., 0.3). This ensures that all computations are made along a fixed distance of travel.

Given that the vehicle may or may not need to decelerate when approaching the traffic signal, two cases are categorized to develop the Eco-CACC strategies as indicated below. More details of optimum speed profiles during various situations are discussed in (R. K. Kamalanathsharma, 2014; Xia, 2014). Case 1 • Vehicle is able to traverse the intersection during the green indication without

decelerating (either by maintaining a constant speed, or accelerating to a higher speed and then maintaining that speed).

In this case, the vehicle can traverse the signalized intersection without decelerating. In order to reach the maximum average speed to proceed through the intersection, the cruise speed uc during the red indication is defined by Equation (1). Here, tr denotes the remaining red offset time when vehicle arrives dup upstream of the intersection. If vehicle’s initial speed u(t0) is equal to uc, then the vehicle can proceed at a constant speed upstream of the intersection. Otherwise, the vehicle should accelerate to uc by following the vehicle dynamics model presented by Equations (2) through (4). Thereafter, when the signal indication turns green, the vehicle needs to follow the vehicle dynamics model and accelerate from the cruise speed uc to the speed limit uf until the vehicle travels a distance ddown downstream of the intersection. 𝑢" = 𝑚𝑖𝑛 '()*

+,, 𝑢./ (1)

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𝑢(𝑡 + ∆𝑡) = 𝑢(𝑡) + 5(+)67(+)8

∆𝑡 (2)

𝐹 = min '3600𝑓A𝛽𝜂(DE,𝑚+F𝑔𝜇/ (3)

𝑅 = JKL.NK

C(CPA.𝑢K + 𝑚𝑔",RSTTT

(𝑐VS𝑢 + 𝑐VK) + 𝑚𝑔𝐺 (4)

The Eco-CACC system uses a vehicle dynamics model to compute vehicle acceleration behavior. Here, the vehicle acceleration follows the vehicle dynamics model developed in (Yu, Yang, & Yamaguchi, 2015). In this model, F is the vehicle tractive effort; R represents the resultant resistance forces, including aerodynamic, rolling, and grade resistance forces; fp is the driver throttle input [0, 1] (unitless); β is the gear reduction factor (unitless), and this factor is set to 1.0 for light-duty vehicles; ηd is the driveline efficiency (unitless); P is the vehicle power (kW); mta is the mass of the vehicle on the tractive axle (kg); g is the gravitational acceleration (9.8067 m/s2); μ is the coefficient of road adhesion (unitless); ρ is the air density at sea level and a temperature of 15 ◦C (1.2256 kg/m3); Cd is the vehicle drag coefficient (unitless), typically 0.30; Ch is the altitude correction factor (unitless); Af is the vehicle frontal area (m2); cr0 is rolling resistance constant (unitless); cr1 is the rolling resistance constant (h/km); cr2 is the rolling resistance constant (unitless); m is the total vehicle mass (kg); and G is the roadway grade at instant time t (unitless). Case 2 • Vehicle decelerates to a lower speed, and then maintains that speed while traversing the

intersection during the green indication. The vehicle’s speed profile in this case is illustrated in Figure 1. Upstream of the intersection,

the vehicle needs to slow down at a deceleration level a, then cruise at a speed uc to traverse the intersection when the signal just turns green. Downstream of the intersection, the vehicle should accelerate from uc to uf, and then cruise at uf. Since the deceleration level a upstream of the intersection and the throttle level fp downstream of the intersection are the only unknown variables for this case, the optimum speed profile can be calculated by solving the optimization problem described below.

FIGURE 1 Optimum speed profile in case 2.

Assume a vehicle arrives dup at time t0 and passes ddown at time t0+T, and the cruise speed during the red indication is uc, the objective function entails minimizing the total fuel consumption level as: 𝑚𝑖𝑛 ∫ 𝐹𝐶Z𝑢(𝑡)[+R\]

+R· 𝑑𝑡 (5)

uf

Time

Speed

u(t0)

uc

Red phase Green phase

t1 tr t2 t0+Tt0

a

10

where FC(*) denotes the energy consumption at instant t. The constraints can be constructed by the relationships between speed, acceleration, deceleration, and distance, as shown below:

𝑢(𝑡):

⎩⎪⎨

⎪⎧

𝑢(𝑡) = 𝑢(𝑡T) − 𝑎𝑡𝑢(𝑡) = 𝑢"

𝑡T ≤ 𝑡 ≤ 𝑡S𝑡S < 𝑡 ≤ 𝑡V

𝑢(𝑡 + ∆𝑡) = 𝑢(𝑡) + 5Z.*[67ZE(+)[8

∆𝑡𝑢(𝑡) = 𝑢.

𝑡V < 𝑡 ≤ 𝑡K𝑡K < 𝑡 ≤ 𝑡T + 𝑇

(6)

𝑢(𝑡T) · 𝑡 −

SK𝑎𝑡K + 𝑢"(𝑡V − 𝑡S) = 𝑑EA

𝑢" = u(𝑡T) − 𝑎(𝑡S − 𝑡T)

∫ 𝑢(𝑡)+k+,

𝑑𝑡 + 𝑢.(𝑡T + 𝑇 − 𝑡K) = 𝑑(lmn𝑢(𝑡K) = 𝑢.

𝑎8on ≤ 𝑎 ≤ 𝑎8Fp𝑓8on ≤ 𝑓A ≤ 𝑓8Fp

𝑢" ≥ 0

(7)

In Equation (6), the functions F(*) and R(*) represent the vehicle tractive effort and

resistance force as computed by Equations (3) and (4), respectively. According to the relationships in Equations (5) to (7), the deceleration a and throttle level fp are the only unknown variables. amin and amax denotes the minimum and maximum allowed acceleration levels to ensure driving comfort. And fmin and fmax denotes the minimum and maximum throttle levels. A moving-horizon dynamic programming approach is used to solve the problem by listing all the combinations of deceleration and throttle values and calculating the corresponding fuel consumption levels; the minimum calculated energy consumption gives the optimum parameters (Guan & Frey, 2013; R. K. Kamalanathsharma, 2014). Considering that the optimization solution needs to be computed at a rapid frequency (e.g., 10 Hz) for real-time applications, an A-star algorithm is used here to expedite the computation speed (H. A. Rakha et al., 2016). The deceleration speed and the throttle level are considered as constant values in the A-star algorithm. However, given that the optimal solution is recomputed at every decisecond, the acceleration/deceleration level can also be updated every decisecond. Bus Fuel Consumption Model A simple bus fuel consumption model was developed and calibrated in (Wang & Rakha, 2016, 2017). The framework of Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM), which was originally developed for LDVs, was used to develop the fuel model for buses as presented in Equation 8. The vehicle power used in the fuel model can be computed as Equation 9.

𝐹𝐶(𝑡): r𝑎T + 𝑎S𝑃(𝑡) + 𝑎K𝑃(𝑡)K, 𝑃(𝑡) ≥ 0𝑎T 𝑃(𝑡) < 0 (8)

𝑃(𝑡) = '7(+)\ZS\t\T.TTKLuE(+)k[8F(+)

vwTTxy/ ∙ 𝑢(𝑡) (9)

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Where FC(t) denotes the instantaneous fuel consumption rate; αT, αS and αK are vehicle-specific model coefficients, that should be calibrated for each vehicle; 𝜆 is the mass factor accounting for rotational masses, a value of 0.1 is used for HDVs (Feng, 2007); 𝜉 is the term related to gear ratio, which is assumed to be zero due to the lack of gear data; a(t) is the instantaneous acceleration level; R(t) is the resistance forces on the vehicle as given by Equation (4). A regression-based approach was developed in (Wang & Rakha, 2016) to calibrate the VT-CPFM model for buses. Mass field data including instantaneous vehicle speed, fuel consumption rate, latitude, longitude and altitude were collected by test driving the buses around the town of Blacksburg, VA. In order to cover a wide range of real world driving conditions, the test driving routes consisted with two roadway sections: US 460 business (highway with a speed limit of 65 mph) and local streets (with the speed limit from 25 mph to 45 mph). The collected data were divided into two data sets for the test bus. The first data set were used for calibration purpose, which include 60% to 70% percent of the entire data for the test bus, and the remaining data set were used for model validation. The regression-based model fitting can estimate the values of parameters αT, αS and αK in Equation 8. The calibrated bus fuel model was compared with the measurement data and presented very good fitting accuracy as shown in Figure 2.

FIGURE 2 Model validation for bus fuel consumption model.

Field Test of Bus Eco-CACC System Test Environment The connected vehicle controlled facility on the Virginia Smart Road at VTTI was used to validate the performance of the proposed bus Eco-CACC system. The Virginia Smart Road is a 3.5 km (2.2 miles) stretch of road with turnaround loops at either end. Wireless roadside equipment unites are installed at a spacing around 500~600 meters which provides 5.9GHz of short range wireless communications between infrastructure and vehicles. Two mobile roadside equipment sites are also available at the Smart Road. The SPaT information at intersection can be remotely controlled by vehicle location or user input through wireless communication. The layout of the test road is illustrated in Figure 3. The road in the vicinity of the signalized intersection is a two-lane surface roadway (one-lane for each direction). The four-way signalized intersection is located on the center of the figure. The road vertical grades for the downhill and uphill direction are approximately 3 percent. The stop lines for both directions are located on the signalized intersection. The Eco-CACC is activated when the testing vehicle is at 200 meters upstream of the stop line and is deactivated when the testing vehicle is at 200 meters downstream of the stop line. Thus, both of dup and ddown are equal to 200 meters. During the test drive on the uphill direction, the bus can accelerate up 32~34 mph before merging to turnaround 1 if the bus was fully stopped at the intersection. Therefore, the speed limit was set as 30 mph. In order to

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have a fair comparison across different runs, vehicle should drive at 30 mph by entering and leaving the range of the system. Thus, two cones were placed at the 200 meters upstream (the first cone) and 200 meters downstream (the second cone) of the intersection for each direction, so in total there are four cones with drivers being asked to drive at 30 mph to pass the cones.

FIGURE 3 Layout of the Test Road (Source: Google Maps).

In order to test the proposed bus Eco-CACC system in response to different signal timings, four different signal timings were selected for the test. This variable is named hereafter as “red offset”, which represents the remaining red offset time when vehicle enters the test area by passing the first cone. And the selected values for the testing are 10, 15, 20 and 25 seconds. When the testing vehicle is far away and moving towards the signalized intersection, the signal phase is red. Then, the red offset countdown is triggered when the testing vehicle arrives at a distance dup upstream of the intersection, which means that the remaining time for the red phase is the randomly preset value (either 10, 15, 20 or 25 seconds). The green phase is chosen as 25 seconds, which is long enough for the vehicle to pass the downstream distance ddown, even for the case when the testing vehicle encounters a complete stop before the stop line. In total, 30 participants were recruited to conduct the field test. All the participants were voluntary recruited from BT bus drivers, since the test vehicle was a diesel bus provided by the Blacksburg Transit (BT) and BT’s policy required the bus can be only operated by BT bus drivers. Each participant was asked to conduct three different driving scenarios including: 1) scenario 1 – uninformed drive; 2) scenario 2 – informed drive with signal timing; 3) scenario 3 – informed drive with recommended speed. The field test aims to investigate vehicle performances

13

under the combination of different scenarios, road grades and red offset timings, and the details of experimental design and statistical analysis are described on the next section. Each participant randomly repeated two times each of the four red offset values for each driving direction. In addition, 3 scenarios as described below were considered for each participant under dry road surface and day light condition. Thus, each participant had 16 trips for each scenario (48 trips for 3 scenarios), and the total trip number for 30 participants was 1440. It should be pointed out that only the data from dup and ddown were extracted during each trip. Eventually, 1440 sets of trip data were collected as the raw data set to analyze the system performances in the field test. • Scenario 1 (S1) – Uninformed drive:

The driver needs to operate the bus normally by following traffic signal, without any driving assistant systems.

• Scenario 2 (S2) – Informed drive with signal timing: The driver is provided with audio information when approaching the signalized intersection. The audio information provides a countdown of signal timing to the next signal phase, which is used to help the driver to operate vehicle maneuver to pass intersection.

• Scenario 3 (S3) – Informed drive with recommended speed: The driver is provided with audio information with the recommended speed when approaching the signalized intersection. The driver is asked to try his/her best effort to control vehicle speed by following the recommended speed.

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FIGURE 4 Hardware of vehicle onboard units in the bus Eco-CACC system.

A diesel bus provided by BT was used in the field test, and the vehicles onboard units of the proposed bus Eco-CACC system were equipped in the cabinet control box behind driver’s seat as presented in Figure 4. The test bus is a 2014 New Flyer XD40 model with 280 horsepower diesel engine. A differential GPS device was installed on the vehicle front top area to ensure the vehicle can receive accurate location information. A data acquisition system (DAS) customized by VTTI was installed in the control box, which collects GPS data, vehicle data, and SPaT and communicates with a portable laptop to compute recommended speed. All the test data were encrypted and stored in a hard drive disk, which were uploaded to VTTI data service after completing the test. An audio system was chosen for conveying the information in the cases of scenario 2 and 3 because previous researches (Stutts et al., 2005; Young & Lenné, 2010) have proven that visual display can be highly distracting for the driver. In order to ensure that the proposed system can be used for real-time applications, the Eco-CACC computes the optimum speed profile at 10 Hz, which means the optimum speed is re-calculated for every 0.1 second. The average driver perception/reaction time is considered as approximately 1.5 seconds. The latency in the communication system is around 0.5 seconds. Hence, the audio system was preset to convey the information to the driver at intervals of 2 seconds.

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Experimental Design and Statistical Analysis The case study aims to investigate the impact of three factors – scenario, road grade and red indication offset on trip fuel consumption level and travel time. Different experimental design approaches were considered for planning this case study so that the data obtained can be analyzed to yield valid and objective conclusions. The simplest option is that each factor can be randomly assigned for each testing trip when a participant passes the signalized intersection from upstream to downstream. However, practically there are several reasons we cannot conduct the field test in this way. Firstly, we do not want a participant to start with scenarios 3 before the other two scenarios, since it is highly probable that a participant’s driving behavior for scenarios 1 or 2 will be changed after getting the experience of following the optimal speed profile in scenarios 3. Secondly, testing the same road grade in two consecutive trips is not effective from a cost and time perspective. Given that the test site is a loop road starting from turnaround 2 to 1 and eventually returning to turnaround 2, participants will need to drive two loops if we want to test two 3% (or -3%) runs. But participant will only need to drive one loop to test a 3% uphill run followed by a -3% downhill run. Therefore, two factors (scenario and road grade) cannot be randomized in this experiment. Specifically, each participant should start with scenario 1, followed by scenario 2, and lastly scenario 3. The road grade iterates through 3% uphill and -3% downhill by driving along the loop road. The only factor that can be randomized is the red indication offset at the instant the bus is 200 meters upstream of the intersection.

FIGURE 5 Structure of the split-split-plot design.

Considering the constraints that two factors are difficult to change, the split-split plot design was used in this study. The split-split plot design is a type of restricted randomization experimental design, which was originally proposed in the field of agriculture to make the experiment design easier and more cost and time effective (Jones & Nachtsheim, 2009). The split-split-plot design in this study is a blocked experiment with three levels of experimental units as shown in Figure 5. The first level of the experimental units is the whole plot (scenario); the second level is the experimental units within the whole plot, called the split-plot (road grade); and the third level is the experimental units within the split-plot, called the split-split-plot (red indication offset). The red indication offset was the only factor that can be randomized without any effects on the experiment. Given that we had a limited participant pool and all the drivers were bus drivers from Blacksburg Transit, we did not recruit participants by gender or age

25 10 15 25 10 15 20 20

3% Uphill

25 15 10 25 20 15 10 20

-3% Downhill

15 25 10 25 15 20 20 10

3% Uphill

25 25 20 10 20 15 10 15

-3% Downhill

15 25 25 15 10 20 20 10

3% Uphill

25 10 15 20 15 20 10 25

-3% Downhill

Whole Plots(Scenario)

Scenario 1 Scenario 2 Scenario 3

Split-Plots(Road Grade)

Split-Split-Plots(Red Light)

16

groups. The participant effect (variation of driver behavior among participants) was considered as a random effect, so it was not used as a fixed effect factor.

The JMP statistical software was used to analyze the split-split plot experiment results. The results of fixed effect tests from the output of the Fit Mixed report are presented in Table 1, which includes the degrees of freedom, F ratio and p value from the ANOVA test. However, an ANOVA test can only tell if the results are significant overall. To further investigate which pairs of compositions from all the source factors have a significant difference, the Tukey Test (also called Tukey’s Honest Significant Difference test) was used to compare all possible pairs of means (Abdi). For the response variable of fuel consumption, the test results indicated that S1 and S3 are significantly different for both uphill and downhill directions for various red indication offset values. Moreover, the differences between S1 and S2 are statistically significant except for driving in the downhill direction for a 20 and 25 second offset. In addition, the results showed that S2 and S3 are significantly different except for the cases with a 10 second offset for both the uphill and downhill directions of travel.

TABLE 1 Results of Fixed Effect Tests. Response Variable Source DF DFDen F Ratio p value

Fuel Consumption

Scenario 2 146 83.004 <.0001 Red offset 3 1255 1182.769 <.0001 Grade 1 175 7355.448 <.0001 Scenario*Grade 2 175 10.658 0.1872 Scenario*Red offset 6 1255 12.449 0.0685 Red offset*Grade 3 1255 30.084 <.0001 Scenario*Red offset*Grade 6 1255 8.796 0.1236

Travel Time

Scenario 2 146 660.503 <.0001 Red offset 3 1255 8623.917 <.0001 Grade 1 175 131.278 0.0853 Scenario*Grade 2 175 13.477 0.0762 Scenario*Red offset 6 1255 53.352 0.1082 Red offset*Grade 3 1255 0.900 0.4849 Scenario*Red offset*Grade 6 1255 3.029 0.6215

Compared to the Tukey Test results for the response variable of fuel consumption, using

the response of travel during the test showed a slightly different results. The differences between S1 and S3 are statistically significant except for the cases with 10 seconds red offset for both uphill and downhill directions, as well as driving with 25 seconds red offset for uphill direction. Moreover, S1 and S2 are only significantly different under 15 and 20 seconds red offset for uphill direction. For uphill direction, the differences between S2 and S3 are statistically significant except for 10 seconds red offset. For downhill direction, the differences between S2 and S3 are statistically significant only driving under 15 seconds red offset. The test results demonstrated that the bus Eco-CACC system produces significantly different fuel consumption performance compared with S1 and S2 in most cases, by consuming a similar (or less) travel times. The quantitative performance analysis of the field test is presented as below.

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Quantitative Performance Analysis The instantaneous fuel consumption, vehicle speed and location were collected during each trip to calculate the average fuel consumption levels and travel times. Table 2 presents the average fuel consumption levels for one trip (from upstream 200 meters to downstream 200 meters) under different factors of scenario (1, 2 and 3), road grade (3% and -3%) and red offset time (10, 15, 20, 25 seconds). Under the same road grade and red offset time, the fuel consumption levels keep reducing from scenario 1 ~ 3 as presented in the left bar charts in Figure 6. Compared to scenario 1, scenario 2 consumed averagely 13.4% and 6.0% less fuel consumption for downhill and uphill directions, respectively. Compared to scenario 1, scenario 3 consumed averagely 34.2% and 10.1% less fuel consumption for downhill and uphill directions. Note that scenario 3 produced significant amount of fuel savings (2.55 times savings over scenario 2) under downhill direction. It should be noted that 15 seconds of red offset corresponds to the maximum fuel savings (49.1% and 15.1%) for scenario 3 under both uphill and downhill directions. This is resulted by the fact that the bus with Eco-CACC has the maximum speed difference compared to the case of bus without Eco-CACC. Under the case of 15 seconds red offset, drivers will expect to stop when vehicle is very close to the intersection since the signal turns from red to green at the last moment. In scenario 1, drivers usually start to reduce speed quickly when vehicle is around 50 meters away from the intersection, which results in 10~15 mph vehicle speed at the start of green light. But in scenario 3, the bus Eco-CACC system will ask drivers to slow down to around 25 mph at beginning, which results in greater than 25 mph vehicle speed at the start of green light. So the average bus speed in scenario 3 was much higher than the speed in scenario 1 under 15 seconds red offset, which resulted in the maximum savings of fuel in this case. It’s also very interesting to see that the test bus under 3% uphill direction consumed 2 ~ 3 times of fuel compared to driving with similar speed under -3% downhill direction. In total, scenario 3 produced 22.1% of overall average fuel savings compared with scenario 1, and scenario 2 produced 9.7% of overall fuel savings. Such high fuel saving rates by scenario 3 proved that the proposed Eco-CACC system can efficiently save bus fuel in the vicinity of signalized intersections.

TABLE 2 Average Trip Fuel Consumption (FC) Levels.

Direction Red offset (sec)

Scenario 1 FC (liter)

Scenario 2 FC (liter)

Scenario 3 FC (liter)

Difference between S2 and S1 (%)

Difference between S3 and S1 (%)

Downhill

10 0.102 0.072 0.056 -29.7% -44.9% 15 0.179 0.151 0.091 -15.3% -49.1% 20 0.217 0.202 0.161 -6.7% -25.8% 25 0.229 0.224 0.190 -2.1% -16.8%

Uphill

10 0.369 0.356 0.354 -3.5% -4.2% 15 0.424 0.390 0.360 -8.0% -15.1% 20 0.451 0.419 0.399 -7.1% -11.6% 25 0.462 0.438 0.419 -5.3% -9.3%

Downhill Average 0.182 0.162 0.125 -13.4% -34.2% Uphill Average 0.427 0.401 0.383 -6.0% -10.1% Total Average 0.304 0.282 0.254 -9.7% -22.1%

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Table 3 presents the average trip travel times under different scenarios, grades and red offset times. Under the same road grade and red offset time, the similar trend that travel times keep reducing from scenario 1 ~ 3 can be observed in the right bar charts in Figure 6, which means scenario 3 always produced the least fuel consumption levels and travel times. Compared to scenario 1, scenario 2 consumed averagely 2.5% and 3.0% less travel times for downhill and uphill directions, respectively. Compared to scenario 1, scenario 3 consumed averagely 6.9% and 5.3% less travel times for downhill and uphill directions, respectively. Note that travel times under uphill direction are not significantly different from downhill direction. In total, scenario 3 produced 6.1% of overall average travel time savings compared with scenario 1, and scenario 2 produced 2.8% of overall travel time savings. The test results in Table 1 and 2 proved that the proposed bus Eco-CACC system can efficiently reduce fuel savings while keeping a fairly amount of travel time savings at the same time.

TABLE 3 Average Trip Travel Times.

Direction Red offset (sec)

Scenario 1 TT (sec)

Scenario 2 TT (sec)

Scenario 3 TT (sec)

Difference between S2 and S1 (%)

Difference between S3 and S1 (%)

Downhill

10 30.4 29.6 29.4 -2.9% -3.3% 15 34.1 33.1 30.6 -2.8% -10.1% 20 40.1 39.0 36.7 -2.9% -8.5% 25 45.7 45.1 43.1 -1.4% -5.6%

Uphill

10 31.1 30.2 30.0 -2.9% -3.5% 15 35.5 34.3 32.0 -3.3% -9.7% 20 42.0 40.3 39.9 -3.9% -5.0% 25 47.3 46.4 46.0 -2.0% -2.7%

Downhill Average 37.6 36.7 35.0 -2.5% -6.9% Uphill Average 39.0 37.8 37.0 -3.0% -5.3% Total Average 38.3 37.2 36.0 -2.8% -6.1%

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FIGURE 6 Compare test results for fuel levels and travel times.

Sample vehicle speed profiles of a selected participant for downhill and uphill direction under various red offset timings are presented in Figure 7 and Figure 8. All speed profiles have the similar starting and ending speeds around 30 mph, so the comparison between different scenarios are fair since vehicle speed values in between were affected by the settings of uninformed or informed driving in each scenario. For scenario 1, it can be observed that vehicle resulted in completely stop for 20 and 25 seconds red offset. Scenario 2 also had a completely stop under 25 seconds red offset. Apparently, scenario 3 produced much smoother speed profiles compared with other scenarios. The sample speed profiles demonstrated the benefits of the proposed bus Eco-CACC system in assisting the bus to drive smoothly in the vicinity of signalized intersections and therefore reduce fuel consumption and vehicle delay.

(a) (b)

20

(c) (d) FIGURE 7 Vehicle speed profiles of a selected participant for downhill direction under various red offset timings: (a) 10 seconds; (b) 15 seconds; (c) 20 seconds; (d) 25 seconds.

(a) (b)

(c) (d)

FIGURE 8 Vehicle speed profiles of a selected participant for uphill direction under various red offset timings: (a) 10 seconds; (b) 15 seconds; (c) 20 seconds; (d) 25 seconds.

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Driving Simulator Test Test Environment This study implements an ESC system in the full-scale 3D driving simulator (DS) with VR-Design Studio software provided by Forum8 Company (http://www.forum8.co.jp) to study drivers’ behavior in the vicinity of a signalized intersection in the presence of speed guidance. The hardware of the DS is like a real car including the cockpit, ignition key, automatic transmission, acceleration and brake pedals, steering wheel, three surrounding monitors (for front and rear, right and left views), safety seat belt, wiper and hazard button (Figure 9). VR-Design Studio software has the ability to view the surrounding landscape with 3D buildings, vehicles, trees, etc.; allows the visual examination of alternative project options; and animates the vehicle movements and driving simulation. The software is capable of collecting driver speed, acceleration, and location data and includes some connected vehicle capabilities. The software can create networks with real-world features such as traffic signals, road markings, and intersections. It is also possible to create different scenarios under various traffic conditions and weather, and also offers a realistic driving scene as shown in Figure 10.

We developed a road segment and a signalized intersection with nine scenarios of different road characteristics, traffic conditions and information provision to investigate drivers’ behavior and CO2 emissions reduction. Each scenario would take one to two minutes to drive. As shown in Table 4, Scenario 1 is the base scenario in which no information is provided to benchmark participants’ driving behavior in the vicinity of a signalized intersection in the absence of information. Scenarios 2-7 recommend a speed that allows participants to pass through the signalized intersection without stopping if they follow the recommended speed. In Scenarios 8 and 9, a countdown traffic signal is implemented to help participants adjust their speed based on the signal time. The even-numbered scenarios with information provision are on an uphill road, while the odd ones are on a downhill road. Scenarios 1-3 and 8-9 have no traffic and the road has only one lane to analyze the pure effect of information provision; however, there is mild traffic in Scenarios 4-7 to analyze the effect of traffic on driver compliance behavior. Scenarios 4-5 and 6-7 test the effect of maneuverability due to additional lanes in the presence of traffic.

We asked participants to drive each scenario several times, as shown in Table 4, and analyzed the data for the best experience as well as the average of all experiences for each scenario. The best experience was different for each participant and was not necessarily the last run. All participants were asked to fill out two survey questionnaires. The first one focused on socioeconomic features of the participants such as age, gender, work status, educational level, income level, and household size. The other survey was conducted after the driving experiment and addressed the usefulness of the speed information provided as well as their simulator sickness experience if any.

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FIGURE 9 Driving Simulator.

FIGURE 10 Snapshot of driving simulator environment.

23

TABLE 4 Scenario descriptions

Data Collection IRB approval was obtained before data collection. We distributed flyers about the driving experiments within the Morgan State University campus and Baltimore metro area; we also posted the flyers on advertising websites and social media to obtain a fairly unbiased sample. Participants were paid $15 per hour of driving the simulator. Fifty-eight participants accomplished driving simulator experiments; their socioeconomic features are presented in Table 5. Unfortunately, we were unable to recruit enough female participants.

A speed limit of 40 mph was set in the driving simulator. Participants were supposed to drive at 40 mph and change their speed in response to the information provided via ESC (Scenarios 2-7) to go through the signalized intersection without stopping. The goal of the study is to measure the ability of drivers to follow the speed recommendation, rather than their willingness to follow.

Thus, before the start of the driving experiment, we suggested to the participants that they follow the provided guidance during their experiment in order to traverse the intersection without stopping, which reduces emissions. However, there was no instruction to force them to follow the guidance. Some participants were able to follow the provided speed guidance while others were not. For most participants, it took them a while to be able to follow and adjust their speed to the recommended speed. All participants drove each of the nine scenarios several times (Table 5) and their speed behavior was analyzed for each scenario, including without any information (base), with ESC, and countdown traffic signal.

Scenario

Information Type

Traffic Type

Road condition

Number of lanes

Number of runs

1 No Information No Traffic Uphill 1 lane 10 2 Eco- Speed No Traffic Uphill 1 lane 10 3 Eco- Speed No Traffic Downhill 1 lane 10 4 Eco- Speed Mild Traffic Uphill 1 lane 3 5 Eco- Speed Mild Traffic Downhill 1 lane 3 6 Eco- Speed Mild Traffic Uphill 3 lanes 3 7 Eco- Speed Mild Traffic Downhill 3 lanes 3 8 Countdown No Traffic Uphill 1 lane 3 9 Countdown No Traffic Downhill 1 lane 3

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TABLE 5 Participants' Socioeconomic Characteristics

Characteristics Options Percentages Characteristics Options Percentages

Gender Male 64%

Age

18-25 35% Female 36% 26-35 40%

Education level

High school or less 29% 36-45 10%

Associate degree 21% 46-55 3%

Bachelor’s degree 29% 56-65 10%

Post-graduate 21% >65 2%

Work status

Unemployed 28%

Income level

No answer 17% Work part-time 38% < $20K 19%

Work full-time 34% $20K-

$30K 12%

Household size

1 28% $30K- $50K 21%

2 24% $50K- $75K 14%

3 22% $75K- $100K 10%

≥ 4 26% > $100K 7%

Test Result and Discussion Following recommended speed analysis In order to find the percentage of drivers who follow the recommended speed by their socio-demographic characteristics, we utilized an ANOVA and post hoc Tukey. The result of the ANOVA (Table 6) shows that younger participants (less than 45 years old) are more successful in following the recommended speed than older participants (over 45 years old) among men and women. Table 7, shows that age significantly affects following speed, while gender dose not. Since it is hard to reach exactly the recommended speed, we considered the participants to be complying if their speed was within 3 miles per hour of the recommended speed (e.g., recommended speed +/- 3 mph).

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TABLE 6 ANOVA Results for following speed

Gender Age Groups Mean Std. Deviation F Sig.

Male Less than 45 51.788% 25.8045% 3.57 0.01

Greater than 45 43.744% 16.9077%

Female Less than 45 46.845% 27.1700%

Greater than 45 38.491% 21.8143%

TABLE 7 Tukey’s Results for following speed

Mean Square F Sig.

Corrected Model 2692.137 4.165 0.006 Intercept 469448.893 726.231 0.000 Gender 1491.790 2.308 0.129 Age 3858.502 5.969 0.015 Calculating CO2 emissions In this study, among several methods to calculate CO2 emissions based on vehicle movements, we used the Virginia Tech Comprehensive Power-Based Fuel Consumption Model (VT-CPFM) proposed by H. A. Rakha et al. (H. A. Rakha et al., 2011).

TABLE 8 ANOVA analysis of CO2 Emissions by Scenarios

Scenarios Types Mean Std. Deviation

Std. Error F Sig.

Base Scenario 108.3095 18.69356 3.89788 3.609 0.30

ESC Scenario 97.1522 13.35615 1.75375

Countdown Scenario 103.9975 22.76840 3.04255

To identify the differences in CO2 emissions levels in different scenarios of speed guidance (no guidance, voice-activated ESC speed guidance every two seconds, and countdown signal timing) and most effective scenarios on the basis of emissions reduction, an ANOVA analysis was conducted. We compared different scenarios of information provision that have the same traffic conditions and road characteristics, which are Scenario 1 (no guidance), Scenario 2 (ESC system) and Scenario 8 (countdown system). Table 8 shows that the CO2 emissions level is

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significantly different in different scenarios and as was expected the CO2 emissions are highest in base scenarios and lowest in ESC scenarios.

A Tukey Post HOC analysis as shown in Table 9 shows that there is a statistically significant difference in the mean of emissions between the base scenario and ESC scenario; the average CO2 emissions is reduced by 10.3% in the ESC compared to no guidance. However, the average CO2 emissions are not significantly different in countdown timing and no guidance. Thus, the ESC is the most effective way of reducing the CO2 emissions.

TABLE 9 Tukey's Post Hoc Analysis of Emissions in different Scenarios

Scenarios Comparison Mean Difference Std. Error Sig.

Base Scenario ESC Scenario 11.15732* 4.58375 0.043 Countdown Scenario 4.312065217 4.60693 0.619

ESC Scenario Base Scenario -11.15732* 4.58375 0.043 Countdown Scenario -6.845258621 3.48498 0.125

Countdown Scenario

Base Scenario -4.312065217 4.60693 0.619 ESC Scenario 6.845258621 3.48498 0.125

*. The mean difference is significant at the 0.05 level.

Regression Analysis To identify the relationship between CO2 emissions as a dependent variable and following the recommended speed and variance of vehicle speed as independent variables, we performed two regression analyses separately for uphill scenarios and downhill scenarios. The results of both regressions (Tables 10 and 11) show that there is a negative relationship between CO2 emissions and following the recommended speed; when participants follow the recommended speed provided via ESC the emissions level will be less than when they do not follow it. Also, there is a positive relationship between CO2 emissions and vehicle speed variance; the more the vehicle’s speed varies, the greater the CO2 emissions will be.

TABLE 10 Regression Results for Uphill Scenarios Variable B Sig. (2-tailed) Intercept 96.402 .000 Following the recommended speed -.252 .000 Vehicle speed variance .155 0.000 Adjusted R2: 0.576

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TABLE 11 Regression Results for Downhill Scenarios Variable B Sig. (2-tailed) Intercept 50.886 .000 Following the recommended speed -.203 .000 Vehicle speed variance .195 .000 Adjusted R2: 0.653 Compliance Model Due to collinearity between variables, a Generalized Linear Models (GLM) analysis was performed to find factors affecting drivers’ behavior in following the speed guidance. We defined variable “percentage following” as the percentage of time that the participant was able to follow the recommended speed (recommended speed +/- 3 mph) in ESC scenarios. Among all socioeconomic characteristics and variables related to the scenarios, age, annual income, household size, vehicle’s average speed, and the start time of following the recommended speed (as independent variables) had a significant impact on following percentage (as a dependent variable). It should be mentioned that because of the relationship between household size and household annual income, we considered the interaction between those two variables in the model.

As shown in the Table 12, the results of the model indicate that the older the participants are, the less successful they will be in complying with the recommend speed. Gender had no significant effect in compliance behavior. The average speed of the vehicle has a reverse effect on following behavior. Also, the earlier they start following the recommended speed in each scenario, the more successful their compliance will be. Start time of following the recommended speed could be related to their reaction time. The other significant variable which affects following percentage is annual income per household; the more the income per household, the more successful they will be in following the speed advisory. For example, following the speed advisory is less successful for a person in a household size of two with income less than $20,000 (average of $10,000 per person), than for a person in a household size of four (or more) and annual income of over $100,000 (a rough average of $25,000 per person). Similarly, a person in a household size of two and annual income between $50,000 and $75,000 (a rough average of $31,200 per person) would follow the recommended speed more than a person in a household size of four (or more) and annual income of over $100,000 (a rough average of $25,000 per person). Wealthier participants follow the recommended speed better than the others, probably due to being exposed to in-vehicle technology.

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TABLE 12 Compliance Model Variable 𝜷 Standard

Error Significance

Constant -19.172 6.2621 0.002 Gender Male -0.772 1.970 0.695 Female Reference Category Age -2.162 0.657 0.001 Average Vehicle Speed 2.796 0.138 0.000

Start Time of Following the Recommended Speed

-1.533 0.139 0.000

Annual Income and House Hold Size Annual Income Less than $20,000 and HHS=1

-2.540 4.095 0.535

Annual Income Less than $20,000 and HHS=2

-7.454 4.107 0.070

Annual Income Less than $20,000 and HHS=3

8.783 5.393 0.103

Annual Income Less than $20,000 and HHS>=4

0.620 4.405 0.888

Annual Income $20,000 to $30,000 and HHS=1

0.659 5.715 0.908

Annual Income $20,000 to $30,000 and HHS=2

3.001 4.222 0.477

Annual Income $20,000 to $30,000 and HHS=3

-2.061 4.612 0.655

Annual Income $20,000 to $30,000 and HHS>=4

-10.602 6.195 0.087

Annual Income $30,000 to $50,000 and HHS=1

4.981 4.218 0.238

Annual Income $30,000 to $50,000 and HHS=2

3.237 4.091 0.429

Annual Income $30,000 to $50,000 and HHS=3

1.274 3.895 0.744

Annual Income $30,000 to $50,000 and HHS>=4

0.809 4.560 0.859

Annual Income $50,000 to $75,000 and HHS=1

1.165 4.817 0.809

Annual Income $50,000 to $75,000 and HHS=2

10.900 5.539 0.049

Annual Income $50,000 to $75,000 and HHS=3

-1.415 5.562 0.799

Annual Income $50,000 to $75,000 and 4.373 4.076 0.283

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HHS>=4 Annual Income $75,000 to $100,000 and HHS=1

2.410 5.797 0.678

Annual Income $75,000 to $100,000 and HHS=2

-7.631 5.356 0.154

Annual Income $75,000 to $100,000 and HHS=3

4.650 4.454 0.297

Annual Income More than $100,000 and HHS=2

-3.009 6.0832 0.621

Annual Income More than $100,000 and HHS=3

-1.601 5.984 0.789

Annual Income More than $100,000 and HHS>=4

Reference Category

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