Customized Bus Research Articles

Optimizing Customized Bus Lines Considering Users' Transfer Willingness under Cooperative and Competitive Relationship between Metro and Online Car-hailing

In the context of ‘carbon peak’ and ‘carbon neutrality’, coordinating individual travel demand through multi-modal transportation and guiding travelers towards new shared public transportation (PT) modes is increasingly important. In this paper, we analyze the competitive and cooperative relationship between online car-hailing (OCH) services and metro systems in Nanning, China, and conduct aquestionnaire survey among different types of OCH users. A mixed choice model that considers psychological latent variables is constructed to investigate OCH users’ attitudes and cognitions toward customized buses (CBs). An improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed to identify potential carpooling station sets, and a hybrid genetic-ant colony algorithm (GACA) is designed to solve bi-level programming model for CB line optimization. Case study results indicate an 83.8% overall transfer rate from OCH users to CBs, with the optimized scheme achieving a 69.68% reduction in carbon emissions.

Optimization for Customized Bus Stop Planning, Order Schedule, and Routing Design in On-Demand Urban Mobility

Optimization for Customized Bus Stop Planning, Order Schedule, and Routing Design in On-Demand Urban Mobility

Collaborative optimization of vehicle scheduling and speed regulation for autonomous customized bus systems

Customized bus systems can improve service quality and efficiency by taking advantage of the speed regulation and capacity variation of autonomous bus systems. To address the challenges of customized bus systems design, this study proposes a collaborative optimization model of vehicle scheduling and speed regulation for the autonomous customized bus system. This model minimizes enterprise operation and passenger travel costs by considering mileage constraints, the time window of passenger travel constraints, and demand pairs coverage constraints. Based on the theoretical properties discovered in the joint design model, the problem is decomposed into a set partitioning master problem and route-selection subproblem. An algorithm combining column generation approach and genetic algorithm is proposed to solve the joint design model. To accelerate the calculation in searching for the shortest route for subproblems, hierarchical filtering strategy is used to reduce the cost of finding feasible routes and improve computational efficiency. The case study reveals that the proposed algorithm exhibits better convergence and stability according to the Wilcoxon test. In a numerical experiment with 100 demand pairs, the joint model can be solved by the algorithm proposed in the study optimally with 19% of the computation time required by the exact algorithms. The departure frequency was reduced by 22%, and the operating cost was reduced by 42% compared with those of customized bus lines serviced separately by human-driven buses. Sensitivity analyses were performed on various operating parameters, such as modular bus capacity and speed boundary, across three different scale cases. The results demonstrate that reasonable parameter settings are beneficial for reducing operating costs and vacant seats.

Exploring Potential Customized Bus Passengers Across Private Car Trajectory Data

Exploring Potential Customized Bus Passengers Across Private Car Trajectory Data

Optimizing Customized Bus Routing and Maximum Seat Occupancy Rate Under the Influence of Epidemic Outbreaks

Optimizing Customized Bus Routing and Maximum Seat Occupancy Rate Under the Influence of Epidemic Outbreaks

Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai

This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.

Multi-agent deep reinforcement learning based real-time planning approach for responsive customized bus routes

Customized bus can meet many passengers’ personalized travel demand in a public transportation system by providing an innovative shared travel service. Customized bus offers multiple bus routes that jointly form a route network to serve its passengers. It must frequently adjust the station sequences of each bus route in response to trip cancellations and new trip bookings during its operation. Different from relevant research works in the literature, this paper proposes a multi-agent deep reinforcement learning based real-time planning approach for tackling the multiple customized bus routes planning problem. We model the problem as a multi-agent Markov decision process for the first time in literature where a separate agent is assigned to each route to plan its station sequence. We then develop a new multi-agent system. Each agent in the system is powered by an encoder–decoder neural network that consolidates the station sequence decision policy for each bus route. We employ a policy gradient-based reinforcement learning algorithm to train the network parameters of the multi-agent system so as to maximize the number of passengers served while ensuring the customized bus service quality and minimizing the operating cost of all customized bus routes. On three (six) problem instances in offline (online) scenarios, the trained multi-agent system can significantly outperform several existing algorithms in terms of the total cost, adaptiveness and computation time.

Potential Routes Extraction for Urban Customized Bus Based on Vehicle Trajectory Clustering

Potential Routes Extraction for Urban Customized Bus Based on Vehicle Trajectory Clustering

Modular Autonomous Electric Vehicle Scheduling for Customized On-Demand Bus Services

Modular Autonomous Electric Vehicle Scheduling for Customized On-Demand Bus Services

A Novel Demand-Responsive Customized Bus Based on Improved Ant Colony Optimization and Clustering Algorithms

The customized bus operating mode based on passenger demand is an effective way to solve the problem of bus services in low travel density areas such as urban fringe areas, ensure the profitability of bus enterprises, and promote the development of customized bus and other emerging bus. First, this study introduces the concept and operating principle of customized bus, determines the advantages and disadvantages of customized bus, evaluates the relevant theories of customized bus lines and station planning, and determines the principles of customized bus lines and station planning. Second, according to the characteristics of customized bus, this study proposes a novel customized bus line and station planning method completely based on passenger travel demand, including travel demand data processing, traffic community division, joint station planning, the establishment of a customized bus line planning model, and the solution of the planning model. Finally, the proposed planning method and improved ant colony optimization and clustering are verified by simulation experiments. The experimental results show that the station line planning method proposed in this paper can better realize the line planning of demand-responsive customized bus as well as meet diverse passenger travel needs.

Commuters’ intention to choose customized bus during COVID-19 pandemic: Insights from a two-phase comparative analysis

The Corona Virus Disease 2019 (COVID-19) has affected individuals’ health safety during commuting travel around the world. Therefore, a customized bus service was promoted to improve commuting health safety during the COVID-19 pandemic in China. This study intends to investigate the potential similarities and differences in terms of commuters’ intention to choose customized buses at the beginning of the COVID-19 pandemic (Phase I) and after adjusting to the COVID-19 pandemic (Phase II). A survey was designed for this purpose and conducted twice during the COVID-19 pandemic. A total of 1,285 responses were collected in two phases in Shanghai, China. Structural equation models (SEM) based on the theory of planned behavior (TPB) were developed to analyze the influential factors of the intention to commute by customized buses. Moreover, a multi-group analysis approach was used to determine the heterogeneity among commuters based on their socioeconomic and travel characteristics. Results show that “attitudes” in Phase I and “perceived behavioral control” in Phase II have significantly positive impacts on the intention to choose customized buses for commuting. In service quality, “comfort and convenience” and “public health safety” in Phase I and “operation and efficiency” and “comfort and convenience” in Phase II have a highly-significant positive impact on commuters’ attitudes toward customized buses. Through the multi-group analysis, differences in car ownership and commuting mode after the pandemic in Phase I and gender in Phase II are explored. Model estimation results can be used to assist in formulating policies and prioritizing policies in different phases of the pandemic to develop customized bus services.

Designing and planning sustainable customized bus service for departing attendees of planned special events: A two-phase methodology integrating data-driven and demand-responsive

Planned special events (PSE), such as concerts and sporting events, will seriously affect the normal operations of the transportation system, especially at the egress stage. The huge taxi and ride-hailing trips of departing attendees is one of the prime reasons for traffic congestion, meanwhile, insufficient vehicles and traffic congestion around the venue also lead to excessive passenger waiting time. Customized bus (CB) service is an emerging green sustainable transportation mode, and its characteristics are suitable for solving corresponding problems. This paper designs a novel CB service mode and constructs a corresponding two-phase planning methodology integrating data-driven and demand-responsive. The first phase includes a potential demand identification model based on graph semi-supervised learning and an integrated algorithm to deploy CB stops. The second phase is CB route planning with heterogeneous vehicles based on attendee choices. A concert held in Tianjin, China was selected as case study to evaluate the designed CB service. The results show that compared with the corresponding taxis and ride-hailing trips, the generated CB services can save 37.71% of the average travel time, 36.56% of the expenses per passenger and 89.58% of the spatial road occupancy, which can greatly alleviate traffic congestion, reduce carbon emissions and improve attendee travel.

Day-to-Day Dynamic Traffic Flow Assignment Model under Mixed Travel Modes Considering Customized Buses

In recent years, customized buses (CBs), a new form of public travel mode between bus and car, has sprung up in China. Its characteristics include flexible routes, each person having a seat and point-to-point travel have attracted travelers who seek high-quality travel, especially car travelers, alleviating traffic congestion at peak periods and leading to a change in urban travel modes. In addition to providing new travel modes, an exclusive bus lane (EBL) is also an effective means to alleviate traffic congestion. Therefore, this paper establishes link impedance functions under mixed travel modes considering the EBL, including customized buses on different kinds of links, and then presents a day-to-day dynamic traffic flow assignment model based on stochastic user equilibrium (SUE). Some conclusions were summarized by numerical case studies. First, the parameter of travelers’ sensitivity to route travel time affects the speed of traffic flow evolution. When it increases to positive infinity, the final state of the traffic network moves from SUE to deterministic user equilibrium (DUE). Second, the parameter on the degree of dependence of travelers on previous experience can not only influence the value size of actual travel time, but also influence the direction of actual travel time evolution. Third, conventional buses and customized buses have higher transportation efficiency than cars, but if the proportion of conventional bus travelers is too large, the total travel time of all travelers in the traffic network may increase. Fourth, when travel demands increase, the proportion of travelers who choose public transit is required to increase to achieve minimum total travel time. Lastly, from the perspective of the whole traffic network in any case, the EBL is not always beneficial. It is recommended to set EBLs when conventional buses and customized bus flows are heavy, which can be judged based on the model established in this paper.

Optimization Model and Solution Algorithm for Rural Customized Bus Route Operation under Multiple Constraints

In order to improve the operational efficiency of public transportation systems in rural areas, we investigated the demand-responsive rural customized bus vehicle route optimization problem. First, a two-stage planning model describing the problem in the reservation phase and real-time phase was constructed with the objectives of minimizing the operating cost of the operator and the travel time cost of the passenger, and the passenger time window, vehicle characteristics, rated passenger capacity and the running time of the route were considered in the constraints. Second, a hybrid algorithm solution model combining bat algorithm and adaptive particle swarm algorithm was designed to obtain a more optimal solution. Finally, the effectiveness of the hybrid algorithm on the optimization model was verified by using the actual road network in some townships of Jing’an County, Jiangxi Province, China, and the obtained objective function value was reduced by 5.5%. The results show that the optimization model and hybrid algorithm designed in this paper can be used to provide theoretical references for opening demand-responsive customized bus route operation schemes in rural areas.

Bi-level optimization for customized bus routing serving passengers with multiple-trips based on state–space–time network

The emerging customized bus (CB) can serve personalized trip requests in a more flexible and convenient way, especially for passengers who have multiple-trip requests in a short time period. Considering the CB service for passengers with multiple trips (CSPMT), a loading-state-oriented state–space–time network-based bi-level programming model is proposed to optimize the routing of CBs, with the objectives of maximizing the operational profit and minimizing the travel cost, while considering the characteristics of multiple trips, time windows, capacity and mixed loads. Besides, a nested algorithm combining the genetic algorithm (GA) and the augmented Lagrangian relaxation-based dynamic programming algorithm is proposed. Then, the proposed model and algorithm are verified and analyzed through a Sioux Falls network and a Beijing sketch network. It can be found from the results that the method can optimize a CB routing plan for passengers with multiple-trip requests and for different network scales. The proposed bi-level model and corresponding algorithm can better adapt to passengers’ personalized trip requests, and promote a higher level of public transport service which will attract more residents from private cars to public transportation, ultimately reducing energy consumption and exhaust emissions, and promoting the sustainable development of modern metropolises.

Modeling mode choice of customized bus services with loyalty subscription schemes in multi-modal transportation networks

This study models the equilibrium mode choice of emerging customized bus (CB) services in a multi-modal urban transportation network. Two unique characteristics of CB services are considered: (1) the reservation of a seat for every CB passenger, and (2) the loyalty scheme for long-term CB subscribers. The first characteristic eliminates in-vehicle crowding but imposes tight constraints on CB demand, while the second characteristic encourages passenger loyalty to CB services. An advanced equilibrium mode choice model based on the dogit model, a well-established travel choice model that is widely used to consider captive mode choices, is proposed to account for the choice behavior of passengers loyal to CB services. In contrast, a nested weibit model is used to account for the choice behavior of passengers not loyal to CB services by simultaneously considering the effects of mode similarity and mode heterogeneity on mode choice behavior. The proposed model is formulated as an equivalent mathematical programming problem, which guarantees the existence and uniqueness of solutions. Numerical experiments are conducted to illustrate the properties and applicability of the proposed model in a real-world setting. The results demonstrate the importance of considering passenger loyalty and managing mode similarity and heterogeneity when modeling emerging CB services. The results also show the applicability of the proposed model for evaluating the performance of a network with CB services and the design and pricing of CB operation and subscription schemes.

Integrated optimization of customized bus routes and timetables with consideration of holding control

Customized bus (CB) is an innovative urban transport system that plays a significant role in meeting personalized travel needs of passengers. This paper deals with the CB service design problem, which is the problem of driving a fleet of capacitated vehicles to pick up and deliver passengers, with consideration of holding control that keeps vehicles at stops for a period of time. A mixed-integer linear programming model is proposed to jointly optimize a set of vehicle routes and timetables with passenger assignment scheme. Through decomposing the model into a bilevel one, we combine genetic algorithm (GA) and large neighborhood search (LNS) with embedding branch-and-bound (BB) method, and develop a GA-LNS-BB hybrid algorithm. Furthermore, an order clustering based divide and conquer (OC-D&C) approach is provided for the ultra-large-scale problems, in which the order clustering problem is formulated as solving an integer linear programming. Finally, we conduct several numerical experiments on the Sioux Falls network and a real-world city-scale network to evaluate the performance of the proposed model and solution methods. The results indicate that: (1) holding control could enhance the flexibility of CB service, and significantly increase the CB carrier’s profit; (2) the GA-LNS-BB hybrid algorithm and the OC-D&C approach could provide high-quality solutions in short time for large-scale/ultra-large-scale problems compared to other standard algorithms.

Urban Customized Bus Design for Private Car Commuters

With the deepening of the urbanization process, the ownership of urban private cars continues to increase, resulting in severe urban traffic congestion and environmental problems. The customized bus, as an emerging public transportation mode, is considered an effective means to alleviate the above problems. This article employs electronic registration identification (ERI) data of vehicles to design customized buses for private cars, consisting of two components: 1) discovering private car commuters and 2) designing customized bus schemes. First, based on the spatial–temporal similarity and high-frequency characteristics of commuting trips, we mined the urban private car commuters and their corresponding commuting trips as the demand for customized buses. Then, we constructed the customized bus model, which targets the number of served passengers with the constraints, such as the trip time window, bus capacity, passenger load rate, etc. In the model, intermediate stops are not set to ensure bus punctuality and passenger experience, and buses of various capacities are employed to ensure effectiveness and efficiency. The differential evolution algorithm was utilized to find the optimal solution for the model. In the experiments, we carried out relevant verification based on Chongqing’s one-week ERI data. The experimental results showed customized bus schemes for various cases and verified the superior performance of our algorithm by comparing it with general optimization algorithms. Besides, through numerical calculation and traffic simulation, the excellent potential for customized buses in reducing urban transportation energy consumption and urban road congestion is illustrated.

Built environment as a precondition for demand-responsive transit (DRT) system survival: Evidence from an empirical study

Media coverage of the convenience, flexibility and personalized travel services of demand-responsive transit (DRT) systems masks the fact that these systems have a high failure rate worldwide. The failed cases suggest that DRT does not necessarily survive in all types of human settlements; however, existing studies have failed to give quantitative assessments of how and the degree to which the built environment impacts DRT use. This paper addresses this problem through an empirical study of a successful DRT system in Dalian, China. The results demonstrate a significant impact of the built environment on DRT use. A larger residential population, lower employment density, more residential land uses, less administrative land uses, poor connectivity to road networks and parking supply, better accessibility to different facilities, and long distances from urban business centers encourage DRT use and are therefore conducive to the development of customized bus (CB) systems (routes). The effects of built environment characteristics on DRT use were found to be largely different from those in regular transit systems, indicating that tapping into niche markets that are poorly served by regular transit services is a potential marketing direction for DRT systems. The findings of this study can provide theoretical guidance for DRT operators to identify appropriate market niches and thereby reduce the risk of failure.

Commuting by Customized Bus: A Comparative Analysis with Private Car and Conventional Public Transport in Two Cities

Commuting is a major component in the creation of traffic and travel problems. Thus, more attention should be given to its practice. Private car (PC) transport, the dominant mode of commuting in most of the world’s major cities, creates traffic-related social problems such as traffic congestion, traffic fatalities and injuries, and adverse environmental impacts. This study proposes a novel commuting travel mode—a customized bus (CB) transit system that provides advanced, personalized, and flexible demand-interactive minibus service using Internet, telephone, and smartphone apps. The aim was to assess and compare the performance of CB with PC and with conventional public transport (PT) systems. A methodological analysis framework was constructed to quantify operational performance measures that enable the comparison of the different travel modes. This analysis framework was then applied to two cities— Auckland, New Zealand, and Paris, France—to assess the overall performance metrics of PC, PT, and CB, such as travel costs, travel time, and fuel consumption. This comparison sheds light on the differences between the travel modes, their viability, and their competitiveness. The results of the case study show that PC is the fastest commuting mode, but the travel costs incurred by it are twice as much as for PT and CB and involve higher fuel consumption. CB also can provide a useful alternative for commuter trips in Auckland and Paris. For increased commuter trips, CB proved to be more efficient than the PC and PT modes. Finally, the CB system tends to be more profitable in Auckland than in Paris.