smart mobility
TRANSCRIPT
SmartMobility
Enero 2015
Responsable Transporte y TICDr. Jaume Barceló
Dra. Lidia Montero
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TRANSPORT & SMART MOBILITY EXPERTISE AND RESEARCH INTERESTS
• Public Transportation Planning
• Development of Real-Time Adaptive traffic Control Systems
• Development and testing of Mathematical Models and Optimisation Algorithms for Transportation Planning
• Development, implementation and testing of Microscopic and Mesoscopic traffic simulators.
• Urban logistics
• Real-time fleet management
Experience in applying optimization and simulation models to transportation problems
• New generation traffic and travel forecasting models
• Real-time multimodal personal journey planners• Urban logistics• Real-time Fleet Management• Emergency and disasters management• Agent based simulation• Rapid Prototyping for urban design
ICT & Transports Research interests
Microscopic and Mesoscopic traffic simulation
Founders of TSS (AIMSUN microscopic & mesoscopic traffic simulation)
6
o
Loop detectors /
Magnetometers
Vehicle n Reaches RSU p At time t3
Vehicle n Sends AVL message
At time t0+t
Vehicle n Reaches RSU k At time t1
Vehicle n Reaches RSU m At time t2
Vehicle n Sends AVL message
At time t0+2t
i
Vehicle n Leaves origin i At time t0
RSU-IDy
On-board unit of equipped vehicle n captured by RSU-IDx at time t1
On-board unit of equipped vehicle n re-captured by RSU-IDy at time t2
Data (RSU Id, mobile device identity, time stamp ti) sent by GPRS to a Central Server
RSU-IDx
Data (RSU Id, mobile device identity, time stamp) sent by GPRS to a Central Server
AVL Equipped vehicle sends message (id, position, speed) at time t
V2V exchange
𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆 𝑹𝑺𝑼𝒙 − 𝑹𝑺𝑼𝒚
𝒕𝟐 − 𝒕𝟏
Average speed
Smart City Sensored CityMulti-technological data sources
Traffic Data Analytics
We are working on most of the services required for Smart Mobility or for dealing with traffic from the perspective of analytics, including data filtering, completion and fusion, the interoperability of data and the processing of huge amounts of data, or Big Data.
Statistical & traffic
flow based models to
identify and eliminate
the outlier
observations
Missing data
supply
Procedures of space-
time traffic state
reconstruction from
heterogeneous data
sources
• Combine traditional traffic supervision technologies with the latest available or soon to be available ICT.
• Data Filtering, Merging and Completion Module. – Filtering of data, integration of new types of data that had not traditionally been
used in traffic information systems, especially those that allow real-time treatment of information.
– Development of completion models for missing data coming from the ICTs.– Development of data merging models that combine large amounts of data sources
unprecedented in the field of traffic.
Avanza Competitividad R&D (2010-2012) Program
http://inlab.fib.upc.edu/en/in4mo-advance-information-system-mobility-people-
and-vehicles
In4Mo. Advance Information System for the Mobility of People and Vehicles
• Turning citizens into an active agent in the generation of mobility data using mobile devices
• Probe Person Survey methodology
• Analysis of mobility and urban behaviour
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http://inlab.fib.upc.edu/en/probe-person-survey-upcnet
Electronic Data Collection for Activity BasedDemand Modeling: Probe Person Survey
Source: Electronic Instrument Design and User Interfaces for Activity Based Modeling (Hato & Timmermann - 2008)
Electronic Data Collection for Activity BasedDemand Modeling: Probe Person Survey
– Vehicle Routing Problem Algorithms
• Time-dependent
• Stochastic Demand
– Micro/Meso Traffic Simulation
Real-time Fleet Management
Fleet Management Center Solution
Decision Support System based on the Macro Fundamental Diagram and, through the proper processing of the data from all detectors, allows to identify on real time the present traffic state of a urban area and its evolution. This information is the used, in combination with traffic models, for the implementation of proactive traffic control strategies.
Decision support systems: Traffic Management
Figure 6 Potential use of the Network Fundamental Diagram to support Active Area WideTraffic Management
Strategies
URBAN AREA TO MANAGE
LARGE URBAN OR METROPOLITAN AREA
Origin r
Destination s
Congestion
Alternative recommended
route
GATE-OUT
GATE-IN
QUEUE
Estimation algorithm for 𝒏 𝒌 ADAPTIVE FLOW CONTROL STRATEGY
A
B
Critical Point in the managed area
Allow access Restrict access
C
Real-time Traffic Data
Measurements from sensors Output flows
n(k-1)
Input flow
rates (k)
Point to point instant dynamic ridesharing.
• A pilot test planned in a city in the Barcelona metropolitan area to share private vehicles to go to the train station located in the closest city. Users can demand the transport just a few minutes in advance.
• It uses mobile technology and a tracking server. The main challenges of this project are not technological but related to social and security issues.
More information: http://inlab.fib.upc.edu/en/dynamic-ridesharing
Dynamic ridesharing
It is not possible to install sensors in ALL streets. It is necessary to look for different ways.
In the same way as weather, Traffic may be calculated and predicted from a limited number of sensors through the use of models
Smart mobility – Traffic forecasting
Example: Weather forecast The model makes use of a small set of data and
provides us with detailed information
Even more: the model can predict the future
evolution of weather conditions
Meteorological
model
New generation forecasting models for high-quality traffic and travel information, short-time real-time predictions
• Current available models and services are useful to provide information for long-term traffic planning or they provide information only based on past information.
• New generation forecasting models are required to provide high-quality traffic and travel information, specially for short term predictions used to plan a trip.
Example of applications/projects:
• Electric vehicle trip planning
• Real-time multimodal trip planning, combining different transport modes
Smart mobility: Traffic and travel forecasting
MesoscopicTraffic
SimulationModels and
theInformationthey supply
forManagement
Network Model
Time-dependent OD matrices
Traffic Control Data
calculate path flows at time t
Perform Dynamic
Network Loading (traffic simulation)
Initial path calculation and selection
Estimate path travel times at time t
DUE Convergence criteria(Rgap ) satisfied
YES
STOP
NO
Estimate the new path sets according to the computational algorithm for equilibrium (MSA, Projection…) adding new paths or removing existing ones for each OD pair and time interval
MAIN OUTPUTS
- Time dependent flows- Time dependent travel times- Queue dynamics- Congestion dynamics
Velocidad en los arcos Tiempo de viaje de los arcosLink Speed Map Link Travel Times
Alternative paths and forecasted path travel times
COMPLETE NETWORK INFORMATION
P&R
P&R
Interactive, integrated, multimodal, real-time decision support system (pre-trip, in-trip)
Data interoperability
Real-Time Advanced Journey Planner
Tool/Method to support agile low cost urban design decisions.
Used to optimize the location of elements such as traffic sensors, eVehicle charging points, accessibility analysis or location of emergency services, etc.
It answers the questions:
• How many?
• Where to locate them?
Based on research on Location Problems.
Rapid Prototyping for urban design
http://inlab.fib.upc.edu
+34 93 401 69 41
c/ Jordi Girona 1-3
Campus Nord. Edifici B6
08034 Barcelona
Spain
Twitter: @inLabFIB
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