TY - JOUR

T1 - Travel demand estimation in urban road networks as inverse traffic assignment problem

AU - Krylatov, A.

AU - Raevskaya, A.

AU - Zakharov, V.

N1 - Publisher Copyright: © 2021 A. Krylatov et al., published by Sciendo 2021.

PY - 2021/6/1

Y1 - 2021/6/1

N2 - Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.

AB - Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.

KW - Congestion

KW - OD-matrix estimation

KW - Primal and inverse problems

KW - Traffic assignment problem

KW - Travel demand estimation

UR - http://www.scopus.com/inward/record.url?scp=85114023127&partnerID=8YFLogxK

U2 - 10.2478/ttj-2021-0022

DO - 10.2478/ttj-2021-0022

M3 - Article

AN - SCOPUS:85114023127

VL - 22

SP - 287

EP - 300

JO - Transport and Telecommunication

JF - Transport and Telecommunication

SN - 1407-6160

IS - 3

ER -