Yongjie Lin

Real-Time Bus Arrival Time Prediction: Case Study for Jinan, China

Providing real-time bus arrival information can help to improve the service quality of a transit system and enhance its competitiveness among other transportation modes. Taking the city of Jinan, China, as an example, this study proposes two artificial neural network (ANN) models to predict the real-time bus arrivals, based on historical global positioning system (GPS) data and automatic fare collection (AFC) system data. Also, to contend with the difficulty in capturing the traffic fluctuations over different time periods and account for the impact of signalized intersections, this study also subdivides the collected dataset into a bunch of clusters. Sub-ANN models are then developed for each cluster and further integrated into a hierarchical ANN model. To validate the proposed models, six scenarios with respect to different time periods and route lengths are tested. The results reveal that both proposed ANN models can outperform the Kalman filter model. Particularly, with several selected performance indices, it has been found that the hierarchical ANN model clearly outperforms the other two models in most scenarios.

Transit Priority Strategies for Multiple Routes Under Headway-Based Operations

This paper presents a transit signal priority (TSP) model designed to consider the benefits both to bus riders and to intersection passenger car users. The proposed strategy, which is mainly for headway-based bus operations, offers the responsible agency a reliable way to determine the optimal green extension or red truncation duration in response to multiple bus priority requests from different routes. The control objective is to minimize bus passenger waiting time at the downstream bus stop while ensuring that the delays for all passengers are not increased. In tests that used field data from Jinan, China, the proposed strategy showed promise in reducing bus passenger waiting time and total intersection delay. Further exploration with simulation experiments for sensitivity analysis found that TSP is most effective if the ratio between bus and passenger volumes exceeds a threshold of 2%.

A bi-level multi-objective programming model for bus crew and vehicle scheduling

This paper focuses on the analysis of bus crew and vehicle scheduling under the management system commonly-seen in China. With considerations of management rules in practice and related regulations in China, this study proposes a bi-level multi-objective programming model for optimizing the crew and vehicle scheduling for the operation of public bus system. The developed model first estimates the lower bound of the global minimum number of drivers and buses needed in each week. The upper-level model minimizes the difference between the solution and the estimated lower bound and determine the number of vehicles needed; and then the lower-level model tries to minimize the number of drivers needed in each day. A branch and bound solution method has been developed to solve the proposed NP-Hard model, and has been programmed to achieve the computer aided crew and vehicle scheduling. A case study based on four different timetables demonstrated that the estimated lower bounds are with 13% of the global optimization that can ben achieved by the proposed approach. The implementation of the developed method can efficiently reduce the operation cost and support the automated intelligent scheduling for the Advanced Public Transportation System.

Variable Speed Limit Control for Delay and Crash Reductions at Freeway Work Zone Area

AbstractImproving operational efficiency and safety on freeway segments has long been recognized as a priority in traffic communities. This study presents a comprehensive variable speed limit (VSL)...

Interval Optimization for Signal Timings with Time-Dependent Uncertain Arrivals

Contending with excessive delays at signalized intersections due to traffic flow fluctuation has been recognized as one of the most challenging issues for traffic researchers and engineers. Due to the uncertainty of vehicle arrivals, a signal timing plan optimized with a fixed demand pattern may lead to ineffective control. In response to this need, this study adopts the theory of interval analysis and defines a set of demand intervals to represent the demand fluctuations. Depending on the demand interval patterns, an optimization model is proposed to maximize the overall robustness of signal design while maintaining an acceptable level of efficiency. A recursive two-stage solution procedure is also developed to solve the optimization problem. To ensure the global optimization, a modified branch-and-bound algorithm is developed for the exploration of solutions. The extensive experimental analyses in comparison with the deterministic optimization model reveal that the proposed model is quite promising for applications, especially under highly fluctuated demand patterns.

Passive transit signal priority for high transit demand: model formulation and strategy selection

Abstract With the increasing of transit vehicles in urban networks, traditional active transit signal priority (TSP) control may fall short of efficiency and bring negative impacts to non-priority approaches due to overusing priority grants. To reduce the frequency of activating TSP under high bus volumes, this study presents a new model, named INTEBAND, to facilitate the platooning of both passenger-cars and buses along arterials. The model integrates the conventional signal progression model with bus progression model to balance the travel delay of car-users and bus-passengers. Taking Jingshi road in Jinan for example, this study employs VISSIM for model evaluations. Further sensitivity analysis reveals that INTEBAND could clearly outperform the two conventional models in reducing network person delay when the ratio of bus-passengers and car-users exceeds a threshold of 1.5. Moreover, a multi-classifier model based on simulation results is to conveniently answer when INTEBAND can yield more benefit than conventional progression models.

Transit Signal Priority Control for Multi-Conflicted Routes under Headway-Based Service

This paper presents a transit signal priority (TSP) model designed to benefit both bus riders and passenger-car users. The proposed method, mainly for headway-based strategy, provides a reliable way to determine the optimal green extension duration in response to multiple priority requests from different bus routes. Also, a basic method for queue length estimation is presented to evaluate the impacts of TSP control on passenger cars. The control objective is to minimize bus passenger waiting time at the downstream bus stop, simultaneously ensuring the total person delay of entire intersection is not increased. Using the microscopic simulation, the proposed strategy has shown its benefits in reducing bus passenger waiting time and total intersection delay.