editorial safe, resilient, and sustainable transportation...

EditorialSafe, Resilient, and Sustainable Transportation Systems

Yuanchang Xie,1 Pan Liu,2 and Chronis Stamatiadis1

1Civil and Environmental Engineering, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854, USA2School of Transportation, Southeast University, 2 Sipailou, Nanjing, Jiangsu 210096, China

Correspondence should be addressed to Yuanchang Xie; yuanchang [email protected]

Received 2 December 2015; Accepted 2 December 2015

Copyright © 2016 Yuanchang Xie et al.This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Traffic accidents are among the top 10 causes of deathworldwide. They resulted in over 1.3 million fatalities in2012 alone and cause billions of dollars of loss each yearin productivity, property damage, and time and fuel due tocongestion. Traffic safety is becoming increasingly important,particularly in developing countries that are facing rapidurbanization and motorization. In addition to traffic safety,there are many recent examples of transportation systemsbeing disrupted by extreme weather events. Transportationactivities, on the other hand, also contributed substantiallyto these extreme weather events either directly or indirectly.Clearly, it is important to build a resilient and sustainabletransportation system that is less vulnerable to disasters andhas the minimum possible environmental footprint.

In this special issue, several studies investigated highwaytransportation from the operations, resiliency, and logisticsperspectives. X. Zhang et al. developed a cooperative Q-learning algorithm for finding near-optimal path sets formultiple Origin-Destination (OD) pairs simultaneously. Thenew algorithm was compared with the k-shortest path algo-rithm and the Dijkstra’s algorithm. The comparison resultshows that the new algorithm can generate more reliableshortest path solutions that take into consideration failuresof road segments and intersections due to congestion ortraffic accidents. L. Yang et al. modified the desiredminimumgap and structure of the Intelligent-Driver Model (IDM) tobetter account for the real-timemaximum deceleration.Theyapplied the new IDM to Adaptive Cruise Control (ACC) andsimulated it under two road conditions. They found that thenew model can improve the safety and stability of ACC. Itcan also increase roadway capacity. Accurate queue lengthestimation can be very useful in developing effective traffic

control strategies to improvemobility and reduce traffic emis-sions. S Zhao et al. modified the Cell Transmission Model(CTM) for signalized intersection queue length estimation.The new CTM was tested based on the queue length datasimulated by a calibrated VISSIM model. It was found toperformwell under both undersaturated and saturated trafficconditions and can accurately model both queue formingand dissipating processes. Accurate traffic flow predictionsare also important to traffic signal control. A. O. Adewumi etal. proposed a traffic flow prediction model based on ChaosTheory by computing the largest Lyapunov Exponent. Tomodel how intersection and road segment failures propagatein a traffic network, Y. Zhang et al. proposed a Coupled MapLattice (CML) method, based on which simulations wereconducted to evaluate the impacts of various factors on net-work failures. As an essential component of autonomous/self-driving vehicles, Traffic Sign Detection and Recognition(TSDR) systems are very important for traffic safety. S. B.Wali et al. developed a fast automatic TSDR system basedon RGB color segmentation, shape matching, and a supportvector machine classifier. The developed system generatedpromising detection and false positive rates under variouslighting and viewing angle conditions. J. A. Marmolejo etal. studied the design of capacity-constrained distributionnetworks to minimize fixed and transportation costs. Theyformulated the problem as a mixed-integer program andproposed a Benders decomposition algorithm to solve it.The Benders decomposition algorithm was compared withCPLEX. The results suggest that it can closely approximatethe optimal solutions generated by CPLEX with significantlyless computation time and is suitable for solving large-scaledistribution network design problems. C.Wu et al. developed

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2016, Article ID 5696419, 2 pageshttp://dx.doi.org/10.1155/2016/5696419

2 Mathematical Problems in Engineering

a Transportation Supply-Demand Ratio (TSDR) model toquantitatively assess whether an urban area’s transportationsupply canmeet its demand for sustainable development.ThisTSDR model is based on the system dynamic principle andwas evaluated using VENSIM simulation.

Given the rapid development of high-speed rail in China,the aging rail infrastructure in developed countries, andmany high-profile rail accidents, railroad safety has beenreceiving increasing attention globally. P. Xu et al. developed arailroad track condition prediction model and demonstratedits accuracy and robustness using track geometry data.L. Song et al. focused on analyzing the safety and reliabilityof high-speed Electric Multiple-Unit (EMU) trains. Theyintegrated Fault Tree Analysis (FTA) and Petri nets anddemonstrated their effectiveness in analyzing the failuremodes of pantograph of high-speed EMU trains. J. Ye et al.proposed an improved Optimal Velocity (OV) car-followingmodel for moving-block train control. Compared to theconventional OV car-following model, the improved OVmodel takes into consideration uncertainties in distanceheadway measurements and generates safer and more stabletrain control performance.

Extreme weather events have caused many airline flightdisruptions. B. Zhu et al. developed a two-stage stochasticmodel to optimize the recovery process of airline flightdisruptions. The first stage adjusts flight schedules to min-imize delay and flight cancellation cost. The second stagefocuses on minimizing aircraft rerouting cost. The authorsalso developed a simulated annealing algorithm to solve thetwo-stage model. G. Duan et al. focused on aircraft crashmodeling.They improved the Functional ResonanceAnalysisMethod (FRAM) by integrating it with model checking,which allows researchers to use the FRAM for identifyingand simulating all possible ways that a hazard factor maylead to accidents. They demonstrated the enhanced FRAM’seffectiveness by applying it to analyze a two-aircraft crash inItaly.

Public transit plays a major role in battling traffic emis-sions and global warming. To make public transit moreattractive, it is important to have well-developed transitassignmentmodels for optimal transit network design. S. Luoet al. investigated the independence between the travel timesof adjacent transit links, which has been implicitly assumedto be independent in most transit assignment methods.They developed a method to determine the validity of thetravel time independence assumption. They also proposeda capacity-restraint assignment method to handle the casewhere the travel time independence assumption is invalid.

This special issue includes a broad range of topics relatedto safe, resilient, and sustainable transportation systems. Wereceived 47 manuscripts and 14 of them were accepted forpublication, covering highway, rail, air, and transit modes.

Acknowledgments

Wewould like to sincerely thank all authors who contributedtheir papers to this special issue. Also, we are extremelygrateful to all reviewers for their time and outstanding work,

which are critical to the success and quality of this specialissue.

Yuanchang XiePan Liu

Chronis Stamatiadis

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