scholarly journals Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem

2021 ◽  
Vol 22 (3) ◽  
pp. 287-300
Author(s):  
A. Krylatov ◽  
A. Raevskaya ◽  
V. Zakharov
Keyword(s):  
Road Network ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Computational Study ◽  
Demand Estimation ◽  
Absolute Difference ◽  
Actual Problem ◽  
Optimization Program ◽  
Urban Road ◽  
High Urgency

Abstract 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.

Simultaneous Optimization Formulation of a Discrete–Continuous Transportation System

2003 ◽  
Vol 1857 (1) ◽  
pp. 11-20 ◽  
Author(s):  
S. C. Wong ◽  
Y. C. Du ◽  
H. W. Ho ◽  
L. J. Sun
Keyword(s):  
Assignment Problem ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Travel Cost ◽  
Transportation System ◽  
Simultaneous Optimization ◽  
Sensitivity Analyses ◽  
Optimization Program ◽  
Traffic Assignment Problem ◽  
The Continuum

Consider a city with a highly compact central business district (CBD) in which commuters’ origins are continuously dispersed. The travel demand is dependent on the total travel cost to the CBD. The transportation system is divided into two layers: major freeways and dense surface streets. Whereas the major freeway network is modeled according to the conventional discrete network approach, the dense surface streets are approximated as a continuum. Travelers to the CBD either travel on the continuum (surface streets) and then exchange to the discrete network (freeways) at an interchange (ramp) before moving to the CBD on the discrete network, or they travel directly to the CBD on the continuum. Specific travel cost–flow relationships for the two layers of transportation facilities are considered. A traffic equilibrium model is developed for this discrete–continuous transportation system in which for a particular origin no traveler can reduce his or her individual travel cost to the CBD by unilaterally changing routes. The problem is formulated as a simultaneous optimization program with two subproblems. One subproblem is a traffic assignment problem from the interchanges to the CBD in the discrete network, and the other is a traffic assignment problem with multiple centers (i.e., the interchange points and the CBD) in the continuous system. A Newtonian algorithm based on the sensitivity analyses of the two subproblems is proposed to solve the resultant simultaneous optimization program. A numerical example is given to demonstrate the effectiveness of the proposed methodology.

scholarly journals Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7341
Author(s):  
Xueying Song ◽  
Zheng Yang ◽  
Tao Wang ◽  
Chaoyang Li ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Traffic Assignment ◽  
Trajectory Data ◽  
Dynamic Traffic ◽  
Urban Road ◽  
Assignment Model ◽  
Flow Assignment ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin–destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

scholarly journals A Market-Inspired Approach for Intersection Management in Urban Road Traffic Networks

2012 ◽  
Vol 43 ◽  
pp. 621-659 ◽  
Author(s):  
M. Vasirani ◽  
S. Ossowski
Keyword(s):  
Urban Areas ◽  
Road Network ◽  
Large Scale ◽  
Traffic Assignment ◽  
Road Traffic ◽  
Developed Countries ◽  
Transportation Management ◽  
Urban Road ◽  
Assignment Strategy ◽  
The Impact

Traffic congestion in urban road networks is a costly problem that affects all major cities in developed countries. To tackle this problem, it is possible (i) to act on the supply side, increasing the number of roads or lanes in a network, (ii) to reduce the demand, restricting the access to urban areas at specific hours or to specific vehicles, or (iii) to improve the efficiency of the existing network, by means of a widespread use of so-called Intelligent Transportation Systems (ITS). In line with the recent advances in smart transportation management infrastructures, ITS has turned out to be a promising field of application for artificial intelligence techniques. In particular, multiagent systems seem to be the ideal candidates for the design and implementation of ITS. In fact, drivers can be naturally modelled as autonomous agents that interact with the transportation management infrastructure, thereby generating a large-scale, open, agent-based system. To regulate such a system and maintain a smooth and efficient flow of traffic, decentralised mechanisms for the management of the transportation infrastructure are needed. In this article we propose a distributed, market-inspired, mechanism for the management of a future urban road network, where intelligent autonomous vehicles, operated by software agents on behalf of their human owners, interact with the infrastructure in order to travel safely and efficiently through the road network. Building on the reservation-based intersection control model proposed by Dresner and Stone, we consider two different scenarios: one with a single intersection and one with a network of intersections. In the former, we analyse the performance of a novel policy based on combinatorial auctions for the allocation of reservations. In the latter, we analyse the impact that a traffic assignment strategy inspired by competitive markets has on the drivers' route choices. Finally we propose an adaptive management mechanism that integrates the auction-based traffic control policy with the competitive traffic assignment strategy.

scholarly journals Non-linear optimization for continuous travel demand estimation

2021 ◽  
Vol 17 (1) ◽  
pp. 40-46
Author(s):  
Anastasiya P. Raevskaya ◽  
◽  
Alexander Y. Krylatov ◽  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Optimization Problem ◽  
Travel Demand ◽  
Linear Optimization ◽  
Demand Estimation ◽  
Theory And Practice ◽  
Traffic Distribution ◽  
Non Linear ◽  
Fresh Perspective

Models and methods of traffic distribution are being developed by researchers all over the world. The development of this scientific field contributes to both theory and practice. In this article, the non-linear optimization of traffic flow re-assignment is examined in order to solve continuously the travel demand estimation problem. An approach has been developed in the form of computational methodology to cope with the network optimization problem. A uniqueness theorem is proved for a certain type of road network. Explicit relations between travel demand and traffic flow are obtained for a single-commodity network of non-intersecting routes with special polynomial travel time functions. The obtained findings contribute to the theory and provide a fresh perspective on the problem for transportation engineers.

scholarly journals Assessing Urban Road Network Capacity considering Parking Supply and Parking Pricing

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiaowei Jiang ◽  
Muqing Du ◽  
Haisheng Liu
Keyword(s):  
Road Network ◽  
Travel Demand ◽  
Management Strategies ◽  
Network Capacity ◽  
Assessment Model ◽  
Urban Road ◽  
The Road ◽  
Capacity Model ◽  
Congestion Effect ◽  
Parking Pricing

To enhance the assessment of the network capacity for a given urban road system, the effects of the parking management strategies at destination areas are supposed to be considered in the network capacity assessment model. This study provides an extended road network capacity model which takes into consideration both the parking supply and parking pricing at each traffic zone. The network capacity model is formulated as a bilevel programming problem, with the maximization of total trip generation in the upper level and the combined trip distribution and traffic assignment (CTDTA) problem in the lower level. To reasonably characterize the impacts of the parking pricing and parking delay due to the congestion effect, two classes of travel demand are involved in the CTDTA model. An efficient and practical algorithm is provided for the solution of the bilevel network capacity model. Numerical experiments show the advantages of the proposed model and also demonstrate the effect of the parking supply and parking pricing on the assessment results of the road network capacity.

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