This document discusses several methods for urban traffic estimation and optimization. It describes two categories of traffic estimation - queue length and travel time estimation. Methods mentioned include using mobile phones, GPS data from mobile phones, and vehicle-to-vehicle communication. The document also discusses optimizing traffic through vehicle routing and traffic signal control, and references models for traffic optimization, signal control, and dynamic vehicle routing based on real-time traffic information.

1. Urban Traffic Estimation and
Optimization: An Overview
4th October, 2012

2. Urban Traffic Estimation
• Two categories;
– Queue length estimation
– Travel time (TT) estimation or delay estimation
• Methods;
– Mobile phones,
– Mobile phones combined with GPS receivers, also
referred to as probe car data
– “Recently” using V2V communication*
* Laura Garelli et al, “MobSampling: V2V Communications for Traffic
Density Estimation”, VTC, Spring 2011.
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3. Related references
• F. Calabrese et al, Real-Time Urban Monitoring Using Cell
Phones: A Case Study in Rome, IEEE Transactions on ITS,
2011.
• L. Guo et al, An Automatic Traffic Congestion Detection
Method Based on Floating Car Data, 15th World Congress on
Intelligent Transport System, 2008.
• Ryan Herring et al, Estimating Arterial Traffic Conditions
using Sparse Probe Data, 13th IEEE ITS conference, 2010.
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4. Urban Traffic Optimization
• Through routing of vehicles
- [Traffic Optimization in Transport Networks Based on Local Routing,
The European Physical Journal, 2010]
- [Dynamic Vehicle Routing Based on On-line Traffic Information, A
Journal of the Institute for Operations Research & The Management
Science, 2004]
• Through traffic signal control
– Fixed-time method
[A Surrogate Model for Traffic Optimization of Congested Networks:
an Analytic Queueing Network Approach”, 2009]
– Traffic-responsive method
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5. Urban traffic management
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6. A Surrogate Model for Traffic Optimization
of Congested Networks: an Analytic
Queueing Network Approach
C. Osorio and M. Bielrlaire, 2009.
a civil engineering scope

7. Overview
• Optimizing signal plans for peak-hour traffic
• Analytic stochastic network model
• Focusing on fixed-time signal control problem
– Cycle time and all red durations are fixed
– Stage and phase structure is given
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8. Objective and assumptions
• Objective:
– Minimize the average time T, spent in the network by
adjusting the green splits at each intersection
• Assumptions:
– Each queue is modeled as a two-dimensional M/M/1/K
queue
– Service rates of the queues leading to an intersection, are
defined as the capacities of the intersection.
– Formulations of the capacities of intersections are based on
the Swiss National Trans. Standards.
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9. Model
• According to the authors, a job:
1. arrives to a station i,
2. waits if the server is occupied,
3. is served (this is called the active phase),
4. is blocked if its destination station is full (this is called the
blocked phase),
5. leaves the station
• The state is represented by the number of:
– active jobs Ai,
– blocked jobs Bi and
– waiting jobs Wi
• The total number of jobs Ni at a station i = Ai + Bi+ Wi
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10. Discussion
• Urban planning point-of-view
• Focus on fixed-time signal plan method
• Model, state definition and the limitation of
generalization / extension of the model
• No interactivity with the vehicles,
– i.e. no use of V2I nor V2V communications
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11. Traffic-responsive methods
• According to the previous reference:
– Traffic-responsive methods use real-time
measurements
• Three ways;
– Small adjustments to a predefined plan
– Choosing between a set of pre-specified plans
– Taking a decision (optimizing) the switching time(s)
to the next stage. (no cycle time nor splits
optimization)
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12. Traffic Optimization in Transport Networks
Based on Local Routing
S. Scellato et al,
The European Physical Journal, 2010

13. Overview
• Global urban traffic optimization based on local
decisions (at the vehicle level)
• Congestion-aware vehicular-routing as a minimization
problem using graph theory
• Edges represent the streets and nodes represent the
intersections
• The solution takes into account:
– the length of the road (path) and
– the congestion in the outgoing roads
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14. Assumptions and objective
• Assumptions:
– Only local knowledge of congestion is available,
– Drivers know the shortest paths to their destinations,
– Weighted urban graph is simulated using GA (Genetic
Algorithm), i.e. Nagel-Schreckenberg model.
– Edges’ weights represent the road’s lengths
• Objective:
– At each node (intersection), the vehicle considers (1) the
nearby congestion, and (2) shortest path, in order to choose
the next node as a hop on the path to destination.
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15. Dynamic Vehicle Routing Based on
On-line Traffic Information
B. Fleischmann, S. Gnutzmann and
E. SandvoßA
Journal of the Institute for Operations Research &
The Management Science, 2004

16. Overview
• Dynamic routing system for fleet of vehicles
• Planning framework for pickup-delivery problem
• Usage of dynamic data
– Travel time information
– Customers’ orders
• Dynamic routing system contains three subsystems:
– Traffic observation system
– Fleet management system
– Planning system
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17. Source: B. Fleischmann et al, “Dynamic Vehicle Routing Based on On-line Traffic Information”,
A Journal of the Institute for Operations Research and The Management Science., 2004.
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18. System model and assumptions
• Fleet of vehicles serving customer orders in urban area.
• Customers require a transport: from pickup location to a
delivery location in a given time window.
• Central system;
– Aware of the status and location of each vehicle,
– Sends the instructions at any time to any driver,
– The response time of the system shall not exceed one second.
• Orders are arriving at any time,
• Travel times varying with the time of the day according to a
forecast,
• Travel time forecast are updated at random incidents.
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19. MobSampling: V2V Communications for
Traffic Density Estimation
Laura Garelli et al,
IEEE VTC – Spring, 2011.

20. Overview
• Distributed V2V-based method to estimate traffic
density in real-time
• No infrastructure required
• Based on vehicular cooperation
• Usage:
– Local decision by the vehicles on the routes,
– Dynamic traffic light adaptation,
– Monitoring CO2 emissions in different areas
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21. Concept
• The vehicle (called sampler) has two roles:
– Collection info about traffic, i.e. density estimation
– Role switching, i.e. handing over the task to
another vehicle (when about to leave the target
area, or the geographical region where traffic is to
be estimated)
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