Jihui Ma

Synchronizing Public Transport Transfers by Using Intervehicle Communication Scheme: Case Study

Synchronized transfers in public transport (PT) networks play an important role in reducing transfer walking time, increasing PT network connectivity, and improving PT reliability and the attractiveness of the PT service. However, because of the dynamic, stochastic, and uncertain nature of traffic, planned synchronized PT transfers do not always materialize. Missed connections frustrate the PT passengers and reduce potential new users. This research proposed an intervehicle communication (IVC)-based scheme to optimize the synchronization of planned transfers in PT networks. A semidecentralized control strategy was developed for the IVC systems to make the optimization a parallel process. Two operational tactics, changing vehicle speed and holding vehicles at transfer points, were used in the optimization with real-time vehicle speed and location information. A distance-based dynamic speed-adjustment model was developed for updating vehicle running speed under the fixed single-point encounter scenario and flexible road-segment encounter scenario. The impact of the proposed IVC scheme on the total number of direct transfers and the total passenger travel time (TPTT) was investigated with a case study of a PT network from Beijing. Results showed that by applying the proposed methodology, the number of direct transfers was considerably increased by 1,100%, and the TPTT was significantly reduced by 13.2%.

Design and Implementation of Bus Monitoring System Based on GPS for Beijing Olympics

To guarantee the safety of public transport and effectiveness in dispatching buses, overall solution on Beijing Bus Monitoring System (BJ-BMS) based on GPS is put forward in this paper. The system is composed of communication control and GIS subsystems, each of which was independently developed in the beginning and integrated in the end.The key technologies of the system are described in detail. In addition to some fundamental functions such as real-time monitoring, some featured functions tightly combined with the daily operations in Beijing Bus Company are realized in the BJ-BMS. Finally, the status of system implementation and maintenance are introduced, which have proven to be very effective in practical applications during 2008 Beijing Olympics.

Bus Arrival Time Prediction Based on the k-Nearest Neighbor Method

In this paper, a modified k-nearest neighbor (k-NN) method integrating the cluster analysis and principal component analysis is applied to bus arrival time (BAT) prediction using historical bus GPS data. The methodology of the k-NN method based on processed GPS data is presented. To validate the introduced k-NN approach, empirical analysis based on data collected from the bus No. 16 of Beijing public transport holdings, Ltd. (BPT) is performed. The results show that the k-NN method has lower prediction error than the ANN model and is more powerful in the BAT prediction.

Optimal Synchronized Transfers in Schedule-Based Public Transport Networks Using Online Operational Tactics

Synchronized transfers in schedule-based public transport (PT) networks are used to reduce interroute or intermodal passenger transfer waiting time and provide a well-connected service. However, in practice, synchronized transfers do not always materialize because of some stochastic and uncertain factors, such as traffic disturbances and disruptions, fluctuations in passenger demand, and erroneous behavior of PT drivers. As a result, missed direct transfers not only frustrate existing users but also discourage potential passengers from using PT service. This work presents an optimization procedure based on a model predictive control (MPC) to increase the actual occurrence of synchronized transfers in schedule-based PT networks. The procedure aims to reduce the uncertainty of meetings between PT vehicles. MPC uses selected online operational tactics based on real-time data, such as skip stop, speed change, and holding. First a library of operational tactics was built to serve as a basis for the sequential receding horizon control process in the MPC. Then, an event activity network with dynamic moving elements was constructed to represent the logical process of the PT transfer synchronization problem. The MPC procedure for a real-time deployment of operational tactics was explicated. A detailed example was used as an expository device to illustrate the procedure developed, along with a real-life example from Auckland, New Zealand.

A Model for the Stop Planning and Timetables of Customized Buses

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space–time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs.

Graphical Human–Machine Interactive Approach for Integrated Bus Transit Scheduling: Lessons Gained From a Large Bus Company

This paper describes an integrated human–machine interactive bus transit scheduling system developed for the Beijing Public Transport Holdings (Group) Company, Ltd. (BPTG), one of the largest bus companies in the world. The system, which is based on a highly informative deficit function graphical optimization technique, allows schedulers to select computer-generated improvements or interject their own practical considerations. The initial implementation of the new system in two subcompanies of BPTG demonstrates its effectiveness and efficiency with many good features, such as improved integration and coordination of scheduling vehicles and crews, reduced vehicle fleet size, and reduced number of crew duties.

Fairness in optimizing bus-crew scheduling process

This work proposes a model considering fairness in the problem of crew scheduling for bus drivers (CSP-BD) using a hybrid ant-colony optimization (HACO) algorithm to solve it. The main contributions of this work are the following: (a) a valid approach for cases with a special cost structure and constraints considering the fairness of working time and idle time; (b) an improved algorithm incorporating Gamma heuristic function and selecting rules. The relationships of each cost are examined with ten bus lines collected from the Beijing Public Transport Holdings (Group) Co., Ltd., one of the largest bus transit companies in the world. It shows that unfair cost is indirectly related to common cost, fixed cost and extra cost and also the unfair cost approaches to common and fixed cost when its coefficient is twice of common cost coefficient. Furthermore, the longest time for the tested bus line with 1108 pieces, 74 blocks is less than 30 minutes. The results indicate that the HACO-based algorithm can be a feasible and efficient optimization technique for CSP-BD, especially with large scale problems.

Large-Scale Demand Driven Design of a Customized Bus Network: A Methodological Framework and Beijing Case Study

In recent years, an innovative public transportation (PT) mode known as the customized bus (CB) has been proposed and implemented in many cities in China to efficiently and effectively shift private car users to PT to alleviate traffic congestion and traffic-related environmental pollution. The route network design activity plays an important role in the CB operation planning process because it serves as the basis for other operation planning activities, for example, timetable development, vehicle scheduling, and crew scheduling. In this paper, according to the demand characteristics and operational purpose, a methodological framework that includes the elements of large-scale travel demand data processing and analysis, hierarchical clustering-based route origin-destination (OD) region division, route OD region pairing, and a route selection model is proposed for CB network design. Considering the operating cost and social benefits, a route selection model is proposed and a branch-and-bound-based solution method is developed. In addition, a computer-aided program is developed to analyze a real-world Beijing CB route network design problem. The results of the case study demonstrate that the current CB network of Beijing can be significantly improved, thus demonstrating the effectiveness of the proposed methodology.

Potential travel cost saving in urban public-transport networks using smartphone guidance

Public transport (PT) is a key element in most major cities around the world. With the development of smartphones, available journey planning information is becoming an integral part of the PT system. Each traveler has specific preferences when undertaking a trip, and these preferences can also be reflected on the smartphone. This paper considers transit assignment in urban public-transport networks in which the passengers receive smartphone-based information containing elements that might influence the travel decisions in relation to line loads, as well as passenger benefits, and the paper discusses the transition from the current widespread choosing approach to a personalized decision-making approach based on smartphone information. The approach associated with smartphone guidance that considers passengers’ preference on travel time, waiting time and transfer is proposed in the process of obtaining his/her preferred route from the potential travel routes generated by the Deep First Search (DFS) method. Two other approaches, based on the scenarios reflecting reality, include passengers with access to no real time information, and passengers that only have access to the arrival time at the platform are used as comparisons. For illustration, the same network proposed by Spiess and Florian is utilized on the experiments in an agent-based model. Two experiments are conducted respectively according to whether each passenger’s choosing method is consistent. As expected, the results in the first experiment showed that the travel for consistent passengers with smartphone guidance was clearly shorter and that it can reduce travel time exceeding 15% and weighted cost exceeding 20%, and the average saved time approximated 3.88 minutes per passenger. The second experiment presented that travel cost, as well as cost savings, gradually decreased by employing smartphone guidance, and the maximum cost savings accounted for 14.2% of the total weighted cost.

Passenger Travel Regularity Analysis Based on a Large Scale Smart Card Data

Analysis of passenger travel habits is always an important item in traffic field. However, passenger travel patterns can only be watched through a period time, and a lot of people travel by public transportation in big cities like Beijing daily, which leads to large-scale data and difficult operation. Using SPARK platform, this paper proposes a trip reconstruction algorithm and adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel patterns of each Smart Card (SC) user in Beijing. For the phenomenon that passengers swipe cards before arriving to avoid the crowd caused by the people of the same destination, the algorithm based on passenger travel frequent items is adopted to guarantee the accuracy of spatial regular patterns. At last, this paper puts forward a model based on density and node importance to gather bus stations. The transportation connection between areas formed by these bus stations can be seen with the help of SC data. We hope that this research will contribute to further studies.