Optimal design and impact analysis of urban traffic regulations under ambient uncertainty

Original Paper

Abstract

Decision-making in traffic regulations is challenging with uncertainty in the environment. In this research we extend probabilistic engineering design concepts to policy decision-making for urban traffic with variability from field data. City traffic is simulated using user equilibrium and cellular automata. A cellular automata (CA) model is developed by combing existing CA models with tailored rules for local traffic behaviors in Tainan, Taiwan. Both passenger sedans and motorcycles are considered with the possibility of passing between different types of vehicles. The tailpipe emissions from all mobile sources are modeled as Gaussian dispersion with finite line sources. Speed limits of all roads are selected as independent policy design variables, resulting in a problem with 50 dimensions. We first study the impacts of a particular policy-setting on traffic behaviors and on the environment under various sources of uncertainties. The genetic algorithm, combined with probabilistic analysis, is then used to obtain the optimal regulations with the minimal cost to the environment in compliance to the current ambient air quality standards.

Keywords

Optimal policy setting under uncertainty Environmental impact Cellular automata Reliability-based design optimization Uncertainty modeling 

List of symbols

\({\mathcal{A}}\)

Set of all possible paths for a origin–destination pair in traffic equilibrium

\({\mathcal{A}}^*\)

Set of shortest path in traffic equilibrium

cij

Cost of the link from the node i to the node j in traffic equilibrium

Cjstd

Standards of pollutant j

Cj

Concentration of pollutant j of a receptor in Gaussian dispersion model

Cit

Cost per unit time in objective function

Cijf

Fuel cost per unit distance at speed j for vehicle type i

d

Distance to the preceding vehicle

do

Distance to the preceding vehicle on the other lane

do,back

distance to the backward vehicle on the other lane

dc,back

distance to the backward car

D

Distance between traffic signals (signal distance)

fk

Traffic flow of path k in traffic equilibrium

H

Effective stack height in Gaussian dispersion model

l

Distance of driver can look ahead on the same lane

lo

Distance of driver can look ahead on the other lane

lo,back

Distance of driver can look back on the other lane

\({\mathcal{I}}_i\)

Set of all upstream nodes of all links arriving at node i in traffic equilibrium

\({\mathcal{L}}\)

Set of all links in a network in traffic equilibrium

L

Length of each site in CA model

m

Number of vehicle types in objective function

ni

Number of vehicles of type i in objective function

nij

Number of vehicles of type i with speed j in objective function

\({\mathcal{N}}\)

Set of all nodes in a network in traffic equilibrium

\({\mathcal{O}}_i\)

Set of all downstream nodes of all links leaving at node i in traffic equilibrium

\({\mathbb{OD}}\)

An origin–destination pair in traffic equilibrium

p

Random breaking probability

pchange

Lane changing probability

Pf

Failure probability levels

Pr[·]

Probability of ·

q

Total traffic flow of an origin–destination pair in traffic equilibrium

q

Emission rate in Gaussian dispersion model

r

A random number generated from standard uniform distribution

s

Distance to the closest preceding signal

T

Time interval between red and green lights (signal period)

T

Vehicle traffic density in objective function

Ti

Traveling cost of vehicle type i in objective function

u

Average wind speed in Gaussian dispersion model

Uh

Actual wind speed in Gaussian dispersion model

Ux

Wind speed perpendicular to the prevailing wind direction in Gaussian dispersion model

Uy

Wind speed along the prevailing wind direction in Gaussian dispersion model

v

Speed of vehicle

vmax

Maximal speed of vehicle

vmax,C

Maximal speed of a car

vmax,M

Maximal speed of a motorcycle

vM

Speed of the preceding motorcycle in lane M

vCM

Speed of the preceding vehicle in lane CM

Viave

Average speed of vehicle type i

xij

Traffic flow of the link from the node i to the node j in traffic equilibrium

ψ

Prevailing wind direction in Gaussian dispersion model

θ

Actual wind direction

δij,k

Indicator of whether the link ij is on the path k in traffic equilibrium

σy

Horizontal dispersion coefficients

σz

Vertical dispersion coefficients

EF

Emission factor in Gaussian dispersion model

TC

Average traveling cost per unit distance

FC

Average fuel cost per unit distance

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Cheng Kung UniversityTainanTaiwan

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