Ziyou Gao

A Cooperative Scheduling Model for Timetable Optimization in Subway Systems

In subway systems, the energy put into accelerating trains can be reconverted into electric energy by using the motors as generators during the braking phase. In general, except for a small part that is used for onboard purposes, most of the recovery energy is transmitted backward along the conversion chain and fed back into the overhead contact line. To improve the utilization of recovery energy, this paper proposes a cooperative scheduling approach to optimize the timetable so that the recovery energy that is generated by the braking train can directly be used by the accelerating train. The recovery that is generated by the braking train is less than the required energy for the accelerating train; therefore, only the synchronization between successive trains is considered. First, we propose the cooperative scheduling rules and define the overlapping time between the accelerating and braking trains for a peak-hours scenario and an off-peak-hours scenario, respectively. Second, we formulate an integer programming model to maximize the overlapping time with the headway time and dwell time control. Furthermore, we design a genetic algorithm with binary encoding to solve the optimal timetable. Last, we present six numerical examples based on the operation data from the Beijing Yizhuang subway line in China. The results illustrate that the proposed model can significantly improve the overlapping time by 22.06% at peak hours and 15.19% at off-peak hours.

Railway freight transportation planning with mixed uncertainty of randomness and fuzziness

The railway freight transportation planning problem under the mixed uncertain environment of fuzziness and randomness is investigated in this paper, in which the optimal paths, the amount of commodities passing through each path and the frequency of services need to be determined. Based on the chance measure and critical values of the random fuzzy variable, three chance-constrained programming models are constructed for the problem with respect to different criteria. Some equivalents of objectives and constraints are also discussed in order to investigate mathematical properties of the models. To solve the models, a potential path searching algorithm, simulation algorithms and a genetic algorithm are integrated as a hybrid algorithm to solve an optimal solution. Finally, some numerical examples are performed to show the applications of the models and the algorithm.

Traffic experiment reveals the nature of car-following

As a typical self-driven many-particle system far from equilibrium, traffic flow exhibits diverse fascinating non-equilibrium phenomena, most of which are closely related to traffic flow stability and specifically the growth/dissipation pattern of disturbances. However, the traffic theories have been controversial due to a lack of precise traffic data. We have studied traffic flow from a new perspective by carrying out large-scale car-following experiment on an open road section, which overcomes the intrinsic deficiency of empirical observations. The experiment has shown clearly the nature of car-following, which runs against the traditional traffic flow theory. Simulations show that by removing the fundamental notion in the traditional car-following models and allowing the traffic state to span a two-dimensional region in velocity-spacing plane, the growth pattern of disturbances has changed qualitatively and becomes qualitatively or even quantitatively in consistent with that observed in the experiment.

Optimization of Multitrain Operations in a Subway System

Energy efficiency is paid more and more attention in railway systems for reducing the cost of operation companies and emissions to the environment. In subway systems, the optimizations on timetable and driving strategy are two important and closely dependent parts of energy-efficient operations. The former regulates the fleet size and the trip time at interstations, and the latter determines the control sequences of traction and braking force during the trip. Most conventional research optimized the timetable and the driving strategy separately such that global optimality cannot be achieved. In this paper, we analyze the hierarchy of energy-efficient train operation and then propose an integrated algorithm to generate the globally optimal operation schedule, which can get better energy-saving performance. Within the criteria of meeting the passenger demand, the integrated energy-efficient algorithm can simultaneously obtain the optimal timetable and driving strategy for trains, which realizes the combination of the high-level transportation management and the low-level train operation control. The simulation results based on the Beijing Yizhuang Subway Line illustrate that the integrated algorithm can achieve a 24.0% energy reduction for one day, on average. In addition, the computation time is within 2 s, which is short enough to be applied for real-time control system.

Urban traffic congestion propagation and bottleneck identification

Bottlenecks in urban traffic network are sticking points in restricting network collectivity traffic efficiency. To identify network bottlenecks effectively is a foundational work for improving network traffic condition and preventing traffic congestion. In this paper, a congestion propagation model of urban network traffic is proposed based on the cell transmission model (CTM). The proposed model includes a link model, which describes flow propagation on links, and a node model, which represents link-to-link flow propagation. A new method of estimating average journey velocity (AJV) of both link and network is developed to identify network congestion bottlenecks. A numerical example is studied in Sioux Falls urban traffic network. The proposed model is employed in simulating network traffic propagation and congestion bottleneck identification under different traffic demands. The simulation results show that continual increase of traffic demand is an immediate factor in network congestion bottleneck emergence and increase as well as reducing network collectivity capability. Whether a particular link will become a bottleneck is mainly determined by its position in network, its traffic flow (attributed to different OD pairs) component, and network traffic demand.

Models and a relaxation algorithm for continuous network design problem with a tradable credit scheme and equity constraints

The sustainable problems of transportation have become noticeable in the majority of cities worldwide. Many researchers are devoted themselves into traffic congestion. Generally, traffic congestion could be alleviated via increasing road capacity (supply) or reducing traffic (demand). In this paper, we model CNDP which has a tradable credit scheme and equity constraints in order to research on the way of releasing congestion by combining increasing supply and reducing demand. Firstly, the bilevel programming problem is proposed to model the CNDP with a tradable credit scheme. The upper level (the government) chooses optimal capacity enhancement for some existing links to minimize the total system costs under a budget constraint. The lower level chooses the optimal route based on considering the generalized travel cost in which both travel time and credit charging for using the link are involved. And then, considering the inequity problem in terms of equilibrium O-D travel cost and link travel time, the model is proposed by incorporating equity constraints into CNDP with a tradable credit scheme. After presenting a relaxation algorithm, the experiments on Sioux Falls network are illustrated. Finally, conclusion and some future research directions are presented.

Modeling Pedestrian Violation Behavior at Signalized Crosswalks in China: A Hazards-Based Duration Approach

Objective: Pedestrian violation is a major cause of traffic accidents involving pedestrians. The research objectives were to investigate the relationship between waiting duration and pedestrian violation and to provide a qualitative and quantitative analysis of the effects of human factors and external environmental factors on street-crossing behavior. Methods: Pedestrians’ street-crossing behavior was examined by modeling the waiting duration at signalized crosswalk. Pedestrian waiting duration was collected by video cameras and it was assigned as censored and uncensored data to distinguish between normal crossing and violating crossing. A nonparametric baseline duration model was introduced, and variables revealing personal characteristics, traffic conditions, and trip features were defined as covariates to describe the effects of internal and external factors. Results: Pedestrians’ crossing behaviors represented positive duration dependence that the longer the waiting time elapsed the more likely pedestrians would end the wait soon. The violation inclination of most pedestrians increased with the increasing waiting duration, but about 10 percent of pedestrians were at high risk of violation to cross the street. About half of pedestrians would still obey the traffic rules even after waiting for 50 s by the street. Human factors and the external environment played an important role in street-crossing behavior, especially for factors that involved pedestrians’ subjective willingness. Conclusions: The street-crossing behavior of pedestrians was time dependent. Pedestrians behave differently under the effects of various factors. Pedestrian safety interventions that aim at reducing pedestrian injuries may need to consider these effects. The pedestrians’ behavioral modifications, such as enhancing the safety awareness, might be the most efficient means to reducing the likelihood of pedestrian violation, though environmental modifications also worked well in improving pedestrian safety.

Train routing model and algorithm combined with train scheduling.

This paper constructs a train routing model combined with a train scheduling problem, which is a 0–1 mixed-integer nonlinear programming problem. Except for train route choice, the model considers a system of complicated constraints on headway, trip time, meeting-crossing and overtaking between trains, capacity of siding, and so on. Based on the delay information of each train, a route adjustment algorithm is designed to obtain satisfactory route schemes of trains. Moreover, a tabu search procedure is designed to further improve the route schemes. The simulation results show that, relative to the optimal solution, the solutions obtained by the current method exhibit small relative error. The tabu search algorithm exhibits unstable performance because of dependence on the initial solution. Combined with the route adjust algorithm, the tabu search technique can improve the quality and stability of solutions. In addition, the departure order of heterogeneous trains exerts important influences on train route choice.

Control Strategies for Dispersing Incident-Based Traffic Jams in Two-Way Grid Networks

Effective control strategies are required to disperse incident-based traffic jams in urban networks when dispersal cannot be achieved simply by removing the obstruction. This paper develops a selection of such control strategies and demonstrates their effectiveness in dispersing incident-based traffic jams in two-way rectangular grid networks. Using the spatial topology of traffic jam propagation, we apply the concept of vehicle movement ban, which is frequently adopted in real urban networks as a temporary traffic management measure. Four control strategies were developed, which are referred to as single-line control, multiline control, area control, and diamond control. We also explore a combination of these control strategies and evaluate the impact of these control strategies on the changes in traffic jam size and congestion delay. Finally, we simulate the processes of traffic jam formation and dissipation using the cell transmission model and demonstrate the performance of the proposed strategies. Simulation results show that the proposed strategies can indeed disperse incident-based traffic jams efficiently.

An equilibrium model for urban transit assignment based on game theory

Abstract The urban public transport system is portrayed as a special commodity market where passenger is consumer, transit operator is producer and the special goods is the service for passenger’s trip. The generalized Nash equilibrium game is applied to describe how passengers adjust their route choices and trip modes. We present a market equilibrium model for urban public transport system as a series of mathematical programmings and equations, which is to describe both the competitions among different transit operators and the interactive influences among passengers. The proposed model can simultaneously predict how passengers choose their optimal routes and trip modes. An algorithm is designed to obtain the equilibrium solution. Finally, a simple numerical example is given and some conclusions are drawn.