Zhi-Chun Li

A multiclass, multicriteria logit-based traffic equilibrium assignment model under ATIS

This paper presents a multiclass, multicriteria (cost versus time) logit-based traffic equilibrium assignment model in road networks served by advanced traveler information systems (ATIS). All users are differentiated by their own value of time (VOT) that follows some probability distribution. Users of each class, having their own VOT, are further divided into two groups, equipped and unequipped with ATIS respectively. The travel disutility received by each user is defined as a linear bi-criteria combination of travel time and monetary travel cost. It is assumed that all users make their route choices in a logit-based stochastic manner, but the equipped users have lower perception variation on the travel disutility than the unequipped due to the ATIS service. The model is formulated as a fixed-point problem and solved by the method of successive averages in conjunction with logit assignment. Numerical results show that the traditional single-class and/or single-criterion models may overestimate or underestimate the benefit from ATIS services. 2005 Elsevier B.V. All rights reserved.

Modeling Park-and-Ride Services in a Multimodal Transport Network with Elastic Demand

With the rapid development of metro systems in large Asian cities, such as Hong Kong and Shanghai, China, local authorities are developing park-and-ride (P&R) schemes to encourage commuters to reach the cities’ central areas by transferring from private cars to metro at stations with P&R facilities. A network equilibrium formulation can be used to model P&R services in a multimodal transportation network with elastic demand. It is assumed that commuters can complete their journeys by three options: auto mode, walk–metro mode, and P&R mode. The proposed model simultaneously considered commuters’ travel choices on travel mode, route–path, and transfer point, as well as their parking choice behavior. The effects of elastic travel demand, together with passengers’ discomfort in metro vehicles, were explicitly incorporated. The resultant problem can be formulated as an equivalent variational inequality problem. Numerical results showed that the introduction of P&R schemes could bring a positive, neutral, or even negative social welfare increment, and its efficiency depends greatly on the parking charging level and the number of parking spaces supplied at the P&R site and in the urban central area, as well as the metro dispatching frequency and fare.

A Model for Evaluation of Transport Policies in Multimodal Networks with Road and Parking Capacity Constraints

This paper presents a model for evaluation of transport policies in multimodal networks with road and parking capacity constraints. The proposed model simultaneously considers choices of travelers on route, parking location and mode between auto and transit. In the proposed model, it is assumed that auto drivers make a simultaneous route and parking location choice in a user equilibrium manner, and the modal split between auto and transit follows a multinomial logit formulation. A mathematical programming model with capacity constraints on road link and parking facilities is proposed that generates optimality conditions equivalent to the requirements for multimodal network equilibrium. An augmented Lagrangian dual algorithm embedded by partial linearization approach is developed to solve the proposed model. Numerical results on two example networks are presented to illustrate the proposed methodology. The results show that the service level of transit, parking charges, road link and parking capacities, and addition of a new parking location may bring significant impacts on travelers’ behavior and network performance. In addition, transport policies may result in paradoxical phenomenon.

Modeling Impact of Transit Operator Fleet Size Under Various Market Regimes with Uncertainty in Network

A network-based model is proposed for investigating the impact of transit operator fleet size on a network with uncertainty under various market regimes. The market regimes concerned include monopoly, free competition, and oligopoly. In the proposed model, the transit operator aims to determine fleet size to optimize its objective function while accounting for transit passengers’ travel choice decisions that minimize their own perceived disutility of travel. To consider the uncertainty effects on transit networks, the proposed model incorporates the unreliability component of transit services into the passenger disutility function, which is mainly due to variations in in-vehicle travel time and the dwell time of transit vehicles at stops. A numerical example is used to illustrate the application of the proposed model. Numerical results show that transit service unreliability could significantly influence transit system performance and the travel choice behavior of transit passengers.

Optimization of Time-Varying Parking Charges and Parking Supply in Networks with Multiple User Classes and Multiple Parking Facilities

Abstract The optimization of parking charges and parking supply over the time of a day is an important problem in the design of transportation networks. This paper presents a bilevel model to determine the optimal time-varying parking charges and parking supply in road networks with multiple user classes and different types of parking facilities. The upper level of the model aims to maximize the network net benefit in response to the parking charges and parking supply, whereas the lower level is a time-dependent network equilibrium problem with elastic demand. A descent-gradient-based solution algorithm is adapted to solve the model. The numerical results show that the implementation of time-varying parking charges and parking supply is useful to effectively cater to the time-varying demand with different parking needs. The model provides a powerful tool for strategically designing parking locations and evaluating various parking policies.

Sustainable Toll Pricing and Capacity Investment in a Congested Road Network: A Goal Programming Approach

AbstractThis paper addresses the road toll pricing and capacity investment problem in a congested road network in a multicriteria decision-making framework. A goal programming approach is used in which the following four major goals are considered: (1)xa0cost recovery, (2)xa0service level, (3)xa0environmental, and (4)xa0equity. The multiobjective road toll pricing and capacity investment problem is formulated as a bilevel goal programming model. The upper level of the model aims to minimize the deviations from stated goals for a given priority ranking of the goals, while the lower level is the road users’ route choice equilibrium problem. A simulated annealing-based solution algorithm is developed to solve the proposed model. Numerical results show that the priority structure of the goals can significantly affect the road toll pricing and capacity investment decisions. The simulated annealing-based solution algorithm outperforms the sensitivity analysis-based solution algorithm in terms of solution quality. The p...

Modeling an elastic-demand bimodal transport network with park-and-ride trips

This paper presents a network equilibrium formulation for modeling commuters travel choices in a bimodal transport system with park-and-ride (PR (ii) parking/PR and (iii) different transport policies may be to some extent mutually substituted.