Customized Bus Research Articles

Role of Customized Bus Services in the Transportation System: Insight from Actual Performance

After attaining great prevalence from the end of 2013, customized bus (CB) transit services have experienced a huge decline in China. The feasibility of this new bus sharing system is thus being questioned. Therefore, it is imperative to investigate the actual role of CB services in the overall transportation system based on successful cases, as the role of the CB service determines its primary service object, system construction, marketing orientation, and even government function. To examine the role of CB services, this study investigates the practical performance, advantages, and spatial and temporal coverage of a successful CB system based on practical subscription data for more than two years. The results illustrate that the CB service is an eclectic choice that can balance service quality and cost between traveling by traditional public transportation (PT) and private cars/taxis. Even though the travel cost increased to a limited extent, the CB service significantly improved the travel experience in terms of the travel time, travel speed, number of stations, and difference arrival time compared to PT services. The multinomial logit model and regression models demonstrate a significant positive relationship between the relative advantage and amount of demand for the CB services. Furthermore, the CB service primarily serves trips generated during the peak traffic hours of the city and supplements traditional PT service in areas with poor coverage levels.

Empirical Analysis of Factors Influencing Potential Demand of Customized Buses in Shanghai, China

AbstractThe customized bus (CB) is an emerging commute mode in China, and it is operated flexibly with schedules arranged based on online reservations. However, a CB can only be operated with suffi...

CB-Planner: A bus line planning framework for customized bus systems

A customized bus (CB) system is an emerging public transportation that aims to provide direct and efficient transit services for groups of commuters with similar travel demands. Existing CB systems aggregate similar travel demands and plan bus lines manually, which is inefficient and costly. In this paper, we propose a CB line planning framework called CB-Planner, which is applicable to multiple travel data sources. A mathematical programming formulation is proposed to simultaneously optimize bus stop locations, bus routes, timetables and passengers’ probabilities of choosing CB. We then developed a heuristic solution framework that includes a grid-density based clustering method for discovering potential travel demands efficiently, a bus stop deployment algorithm to minimize the number of stops and walking distance, and dynamic programming based routing and timetabling algorithms for maximizing estimated profit. We conduct an experiment on a small-scale network to verify the performance gap between the optimal solution and our proposed heuristic solution. A case study is then conducted on one-month taxi trajectory data in Nanjing, China. The study demonstrates that CB lines generated by our CB-Planner can achieve higher profit compared with baseline methods, and they also provide efficient transit services with short walk distances and small departure time adjustments. The moderate increase in travel time is paid off by the significant savings in travel fare.

Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System

In recent years, to fix the shortcomings of traditional bus service and meet the diversified needs of passengers, a new type of transit system, the customized bus (CB), has been proposed. However, how to define and mine the CB’s demand is still less being addressed. Since the data of bus smart cards can provide more travel information, it makes the mining of potential CB’s demand spots more possible, which can be helpful in CB service design. In order to mine the demand spots more scientifically, this paper, for the first time, quantitatively defines the CB demand characteristics and criteria of selecting potential area, and develops a demand hotspots extraction methodology for CB. The methodology solves two issues primarily. One is how to organize massive smart card data and obtain the space-time pattern and mobility of passenger efficiently; the other is how to mix the CB demand characteristics into the method. This demand spots extraction method can generate multi-style maps, including the heat and origin-destination maps, for spatial cluster of CB’s demand spots in rational areas in terms of the CB demand characteristics based on geographic information system. By using the bus smart card data in Beijing, China, this paper carries out a case study to validate the method. The empirical data mining analysis shows that our proposed method can define demand spots ideally. Our work can provide a valuable reference for decision makers to design CB system.

Route Design Problem of Customized Buses: Mixed Integer Programming Model and Case Study

AbstractIn recent years, the customized bus (CB) has been introduced and popularized in China to improve the attraction and service level of public transportation. A key point of the CB system, the...

Methodology for Extracting Potential Customized Bus Routes Based on Bus Smart Card Data

To alleviate traffic congestion and traffic-related environmental pollution caused by the increasing numbers of private cars, public transport (PT) is highly recommended to travelers. However, there is an obvious contradiction between the diversification of travel demands and simplification of PT service. Customized bus (CB), as an innovative supplementary mode of PT service, aims to provide demand-responsive and direct transit service to travelers with similar travel demands. But how to obtain accurate travel demands? It is passive and limited to conducting online surveys, additionally inefficient and costly to investigate all the origin-destinations (ODs) aimlessly. This paper proposes a methodological framework of extracting potential CB routes from bus smart card data to provide references for CB planners to conduct purposeful and effective investigations. The framework consists of three processes: trip reconstruction, OD area division and CB route extraction. In the OD area division process, a novel two-step division model is built to divide bus stops into different areas. In the CB route extraction process, two spatial-temporal clustering procedures and one length constraint are implemented to cluster similar trips together. An improved density-based spatial clustering of application with noise (DBSCAN) algorithm is used to complete these procedures. In addition, a case study in Beijing is conducted to demonstrate the effectiveness of the proposed methodological framework and the resulting analysis provides useful references to CB planners in Beijing.

Methodology of mixed load customized bus lines and adjustment based on time windows.

Custom bus routes need to be optimized to meet the needs of a customized bus for personalized trips of different passengers. This paper introduced a customized bus routing problem in which trips for each depot are given, and each bus stop has a fixed time window within which trips should be completed. Treating a trip as a virtual stop was the first consideration in solving the school bus routing problem (SBRP). Then, the mixed load custom bus routing model was established with a time window that satisfies its requirement and the result were solved by Cplex software. Finally, a simple network diagram with three depots, four pickup stops, and five delivery stops was structured to verify the correctness of the model, and based on the actual example, the result is that all the buses ran 124.42 kilometers, the sum of kilometers was 10.35 kilometers less than before. The paths and departure times of the different busses that were provided by the model were evaluated to meet the needs of the given conditions, thus providing valuable information for actual work.

SRC

Customized bus (CB) services that operate between residential areas and work places during rush hours have surfaced in many cities to deal with traffic congestion problem. One major challenge of CB services is to design service areas based on user demands. To solve this problem, we extract commute patterns from large-scale taxi GPS data for CB service area design. We present a novel clustering algorithm that embeds origin and destination data to a low dimensional feature space, such that the resulting patterns satisfy both the needs of commuters and operators. The algorithm has been tested on both synthesized and real-world data.

Analyzing Customized Bus Service on a Multimodal Travel Corridor: An Analytical Modeling Approach

Customized bus (CB) service, a new type of public transport service, has emerged in many big cities. The few existing research works on CB have been conceptual and descriptive, based on statistical approaches. This paper analyzes the provision of CB by applying analytical approaches based on demand analysis and network modeling. Several alternative travel modes are considered. The choices of mode and route/service are captured by a logit-type formula, and heterogeneous travelers with a discrete set of values of time (VOTs) are considered. Numerical analysis from one idealized corridor indicates that the introduction of CB service could have a significant effect on mode shift, especially for long-distance trips. Three factors, including CB fare, usage of bus lane, and the out-of-vehicle travel time of CB service, are identified that significantly affect the mode shift performance. Another numerical example on a realistic travel corridor in Beijing is conducted to investigate the influence of CB service on social welfare.

Based on Immune Optimization Algorithm and Hybrid Particle Swarm Optimization Algorithm Research on Customized Bus Station Setting and Route Planning—A Case Study of Customized Public Transport in Wuhan, China

Customized bus is a kind of new type of public transport service which is tailor-made for the people whose travel has a similar location, time, destination and so on. In 2013, it has appeared in China and landed in various cities successively. But the development around the situation is different, the study about this problem is few. Based on the immune optimization algorithm and particle swarm optimization algorithm, combined with the advantages of genetic algorithm, the paper forms a hybrid immune optimization algorithm and hybrid particle swarm algorithm. Considering the custom bus station settings, line planning and other aspects, we take custom bus in Wuhan City, Hubei Province as an example, and try to give a scientific and practical guidance to the development of customized public transport nowadays, so that it can get a further theoretical and practical basis on mathematics in its development and operation.

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.

An Optimization Method of Passenger Assignment for Customized Bus

This study proposes an optimization method of passenger assignment on customized buses (CB). Our proposed method guarantees benefits to passengers by balancing the elements of travel time, waiting time, delay, and economic cost. The optimization problem was solved using a Branch and Bound (B&B) algorithm based on the shortest path for the selected stations. A simulation-based evaluation of the proposed optimization method was conducted. We find that a CB service can save 38.33% in average travel time, 38.33% in average waiting time, and 27.78% in delay penalties, all at an acceptable ticket price. The variations are caused by different degrees of busload. A detailed investigation revealed that a CB can significantly reduce travel time, waiting time, and delays, with the degree of benefits showing a decreasing trend with corresponding increases in busload. This finding indicates that CBs with smaller busloads can more flexibly transport passengers and provide a greater level of benefits. At the same time, a small busload strategy can be implemented more easily and in the near future on a test basis in urban China areas.

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.

Analysis of a new public-transport-service concept: Customized bus in China

In recent years, an innovative mode of public transport (PT) service, known as customized bus (CB), has been springing up across China. This service, providing advanced, personalized and flexible demand-responsive PT, is offered to specific clientele, especially commuters. The present work analyzes, for the first time, the evolution of this new PT concept across 30 Chinese cities where CB systems are currently in operation or under construction. Unlike conventional bus transit service, CB users are actively involved in various operational planning activities. CB personalizes PT service by using interactive and integrated information platforms, such as internet website, telephone and smartphone. The analysis comprises three components: first, a comprehensive examination of the background of CB and its temporal and spatial distribution in China; second, an analysis of the operation-planning process, including elements of online demand collection, network route design, timetable development, vehicle scheduling, crew scheduling, real-time control, and fare design and collection: third, a summary of the results of the examination and analysis, presenting pros, cons and recommendations. The successful implementation of CB in China demonstrates that this new PT service concept can effectively meet the ever-increasing mobility needs of large populations nation-wide. Similarly, the present work can provide a valuable reference for policymakers, academic researchers, PT practitioners and others worldwide.