Customized Bus Research Articles

Time-dependent customized bus routing problem of large transport terminals considering the impact of late passengers

Customized bus (CB) provides convenient door-to-door transportation services to passengers through a subscription-based model. To ease the problem of insufficient taxis and private hire services at large transport terminals, we propose and evaluate a customized CB service for railway stations and airports in China. The proposed CB problem considers a routing problem with time-dependent travel time and late-customers. A periodic optimization approach is adopted to periodically collect passenger demands, optimize customized bus routes for the period, and re-optimize the routes to cater to late-coming customers. The static problem for each period is modeled as a time-dependent customized bus routing problem with time windows and release date (TDCBRPTW-R), which is solved with a hybrid meta-heuristic algorithm combining adaptive large neighborhood search (ALNS) with variable neighborhood descent (VND). An acceleration method is developed for insertion feasibility check for the TDCBRPTW-R. The stability and accuracy of the proposed algorithm is evaluated in the numerical experiments derived from Solomon’s data set. For the periodic re-optimization procedure, four methods are proposed to handle late customers according to different application scenarios. The re-optimization procedure is tested on the road network in Chengdu with passenger data provided by Didi Chuxing. The experiments demonstrate that a loose time window allows the service to serve more passengers and reduces operational costs. Lastly, the experiments also show the effectiveness of the proposed methods in handling late customers.

Integration of conventional and customized bus services: An empirical study in Beijing

In recent years, customized bus (CB) service has become an innovative model for public transportation in China. Its ability to provide advanced, timely, personalized, and flexible service has made it very popular. However, previous studies have ignored its diversion effect: How can CB service help conventional buses reduce on-board crowding during rush hours? In this paper, we optimize conventional and CB buses as a whole based on an empirical study in Beijing. We propose a new model to compare the cost of single conventional buses (Single Con) and the integration of conventional and customized buses (Con+ CB). Passenger diversions from conventional buses to CB ones are specifically considered. To do this we explicitly measure the on-board crowding cost of conventional buses by load factor and distance factor. Real-world data from Beijing, China, are employed to assess the model. We obtained the departure arrangements and the corresponding costs under different demand cases. The results are analyzed and compared. Sensitivity analyses are then conducted to study the effects of line-haul distance and load factor on cost. The results show that (1) adding new modes of transportation does not always significantly reduce the total cost of bus service; (2) CB buses can effectively ease congestion on conventional buses only at high demand densities but not at low densities; (3) crowding cost increases caused by the distance factor far exceed those caused by the load factor.

Customized bus scheme design of large transport terminals with jointly optimization of departure time, vehicle allocation and routing

The customized bus (CB) service of large transport terminals can provide passengers with convenient transfers and door-to-door services, which has the potential to help ease the pressure of arriving passenger flow in large transport terminals. Considering the costs of operators and passengers, this paper establishes a multi-commodity network flow optimization model to simultaneously obtain departure schedule, passenger-to-vehicle assignment and routing under the condition of multiple vehicle types. It provides more passengers with high-quality CB service and obtains higher vehicle capacity utilization. Accordingly, the solution is proposed that combines a branch-and-cut algorithm with grid-density-based clustering. An illustrative example verifies the effectiveness and tests the impact of the departure time-window. The case study of Chengdu city compares the results under different types of vehicle schemes and conducts the sensitivity analysis of penalty and minimum load ratio. The experiments conclude: (i) The proposed algorithm stably improves computation speed (76.38%) without affecting the optimal results; (ii) a loose time-window can serve more people, but the excessively loose one no longer has impacts on the results. (iii) The multi-type of vehicle scheme performs best compared to the single type of vehicle schemes.

An Operation Plan Optimization Model for Integrated Customized and Conventional Bus Services Based on Cost Analysis

A proper public transportation system should be economically efficient to improve its sustainability and competitiveness. The superfluous provision of conventional bus services in areas with a weak or scattered travel demand distribution might result in a waste of transport capacity. This study proposes an operation plan optimization model based on the characteristics and cost composition of conventional and customized bus services, which systematically analyzes the feasibility of fully or partially replacing conventional bus services with customized bus services. A mixed-integer planning model and a hybrid genetic algorithm are used to optimize the operation plan for specific travel demands and supply capacities. Then, a sensitivity analysis of key factors is carried out to identify the optimal operation mode under different circumstances. The results show that with an optimized fleet size, service frequency, and service area, integrated operation modes can reduce operating costs without sacrificing the passenger time.

An Improved Adaptive Large Neighborhood Search Algorithm for the Heterogeneous Customized Bus Service with Multiple Pickup and Delivery Candidate Locations

In order to tackle the congestion and environmental issues, customized bus services are proposed and deployed in metropolitan areas. As emerging public transportation services, customized bus services bring passengers more convenience and accessibility. Besides, conventional customized bus services generally organize homogeneous fleet and single location selection to passengers. In this paper, to enhance the mobility and flexibility of customized buses and increase companies’ profit, we propose a new form of customized bus service with heterogeneous fleets and multiple candidate locations. First, a mixed-integer programming model (MIP) is developed to describe the customized bus problem. Compared with the conventional model, the proposed MIP is involved in the case of one passenger with multiple candidate pickup or delivery locations and can be solved by GUROBI on small scale, quickly and efficiently. Second, an improved adaptive large neighborhood search algorithm ( ALNS i p ) is utilized to address the large-scale problem more efficiently. Time slack calculation method is then designed to optimize vehicle timetables, which provides stable and excellent performance for searching feasible solutions. In addition, we propose two inserting operators to deal with the problem with multiple candidate locations and analyse its influence on the results. Finally, we test the performance of the proposed model and algorithm on the numerical experiments. And they are verified the effectiveness and implication in a small-scale case on a simplified Sioux waterfall network and a large-scale problem in Beijing, China. The result shows that ALNS i p outperforms other algorithms in searching for more satisfying solutions with higher efficiency. However, the GUROBI solver can obtain the solution to small-scale problems within a shorter time than ALNS i p . Furthermore, it can be suggested that the heterogeneous fleets service with multiple candidate locations is helpful to facilitate collaboration among vehicles and optimize pickup and delivery routes in consequence.

Pricing Game between Customized Bus and Conventional Bus with Combined Operational Objectives

Social welfare and profit are two main factors to be included in the operational goal of bus services. It is assumed that conventional bus services are “welfare pro” with more consideration of social welfare, while customized bus services are “profit pro” with more consideration of profit, and the goal of each bus service is to maximize the overall payoffs, namely, the weighted sum of social welfare and profit. A bi-level pricing game is proposed to shed light on the impact of two bus services’ pricing competition on the payoffs when the operational objectives with different weights of social welfare are adopted. A case study shows that the pricing strategies of conventional bus services have a significant impact on the customized bus services. The customized bus operator should consider more “social welfare” to attract customers and to achieve the overall payoffs when the conventional bus service takes a high price strategy, and the customized bus operator can take a strategy of a high price to maintain its overall payoffs when the conventional bus operator maximizes the social welfare. It is found that there exists a trade-off by which both sides obtain the balanced overall payoffs.

Exploring the subscribing behavior of customized bus passengers: Active users versus inactive users

Understanding the mechanism of continuous subscribing behavior is vital to the operation and even survival of customized bus (CB) systems. Capturing the differences in subscribing behavior of active and inactive users and developing targeted and differentiated marketing strategies accordingly will help to improve the user retention rate. The results of this study revealed differences in the subscribing behavior between active and inactive users and the influence of a wide range of factors on the subscription behavior between the two groups, such as area-related and individual characteristics. Travel distance presents an inverted “U” shape effect on the propensity to use CB with the vertex at around 16 km for active users and 28 km for inactive users, which is a further finding of the monotonic positive effect indicated by the existing studies. For every RMB 10 cheaper CB trip, the likelihood that an active user holds a higher “propensity” to use CB services will increase by 8%. The cost advantage will attract novelty-seeking travelers to try CB services occasionally, but cannot convert them into high-frequency users. The passengers who started to subscribe to the CB service in winter are more likely to travel by CB frequently no matter in active and inactive user groups. The findings of this empirical study lay a theoretical foundation for ridership retention and marketing orientation of the customized and demand-responsive transit services.

The One E-Ticket Customized Bus Service Mode for Passengers with Multiple Trips and the Routing Problem

To alleviate the problems of traffic congestion, excessive energy consumption, and the environmental pollution caused by private cars, it is essential to use public transportation (PT). However, passengers making multiple trips in a short time period must repeatedly make travel mode choices, purchase tickets, and wait for buses for each trip, which may negatively affect their preference for PT. In order to improve the attractiveness of PT, especially for passengers requiring multiple trips in a short time period, this paper proposes the one e-ticket customized bus service mode for passengers with multiple trips (OECBSM-PMT) by customized buses (CBs). Besides, a CB-routing optimization model for the OECBSM-PMT is also developed in this paper, formulated as a mixed-integer linear programming based on a vehicle routing problem with pickup and delivery and time windows (VRPPDTW). The model aims to maximize the profit and minimize the costs of operation with considering passengers with multi-trip requests, homogeneous CB fleets with pickup/delivery-time-window constraints, and mixed loads. A service effectiveness identification procedure based on genetic algorithm (GA) is proposed to cope with the calculation considering the characteristics of passengers with multiple trips. Finally, the proposed model and algorithm are verified and analyzed using the case of the 2022 Beijing Winter Olympic Games. It can be found from the results that the method can provide an optimized CB route plan and timetable, and the algorithm GA-I obtains better solutions than other solving strategies in most instances. The proposed OECBSM-PMT and corresponding optimized method can better adapt to diverse travel demands, significantly improve the convenience for passengers, especially those making multiple trips in a short time period and will eventually promote a higher level of public transport service.

A Survey of Traditional and Customized Bus Transportation Planning Methodologies

Transportation planning is an area of public policy that is increasingly recognized for having a significant impact on human health and well-being. Passengers all across the world are choosing bus transit as one of the most cost-effective ways of transportation. The number of passengers who use this mode of transportation is steadily increasing. According to statistics, the bus was India's most popular mode of transportation in 2014. A bus was viewed as a mode of transportation by 66 percent of families in rural areas and 62 percent of households in urban areas. With increased demand, there is a concern about efficiently organizing this service. Because a lack of planning can generate major problems in the real world, such as traffic jams and high operating expenses, it is a source of concern for corporate and government entities who provide this service. In this paper, a review on various bus transit planning approaches and stages and methodologies used in each stage of the customized bus planning strategy is presented. This study will assist bus service organizing entities, whether private or public, in efficiently organizing bus service

How Does COVID-19 Affect People's Willingness to Customized Bus Travel

How Does COVID-19 Affect People's Willingness to Customized Bus Travel

Factors Affecting the Demand for Customized Bus: Empirical Evidence Using Machine Learning

Factors Affecting the Demand for Customized Bus: Empirical Evidence Using Machine Learning

Dispatching Design for Customized Bus of Hybrid Vehicles Based on Reservation Data

Proper vehicle operation and route planning are critical for achieving the best match between bus operation and passenger services. In order to enhance the attractiveness of public transportation, a new type of the public transportation dispatching method based on passenger reservation data is proposed. This mode can meet the requirements of multiple lines in urban centers during peak hours, which can realize direct service between two stations. Then, taking the lowest operating cost of the enterprise and the lowest passenger waiting cost as the optimization goal, a customized dynamic dispatching model of multiline and hybrid vehicles was established. Finally, a calculation example is designed and the genetic algorithm is used to solve the model. The results show that the hybrid vehicle solution is more reasonable than the traditional single-vehicle solution and reveal that the model is feasible to optimize scheduling plan. The conclusions obtained in this research lay a theoretical foundation for APP setup and operation plan formulation.

Optimizing Customized Transit Service considering Stochastic Bus Arrival Time

The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi’an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed.

Study on Customized Shuttle Transit Mode Responding to Spatiotemporal Inhomogeneous Demand in Super-Peak

Instantaneous mega-traffic flow has long been one of the major challenges in the management of mega-cities. It is difficult for the public transportation system to cope directly with transient mega-capacity flows, and the uneven spatiotemporal distribution of demand is the main cause. To this end, this paper proposed a customized shuttle bus transportation model based on the “boarding-transfer-alighting” framework, with the goal of minimizing operational costs and maximizing service quality to address mega-transit demand with uneven spatiotemporal distribution. The fleet application is constructed by a pickup and delivery problem with time window and transfer (PDPTWT) model, and a heuristic algorithm based on Tabu Search and ALNS is proposed to solve the large-scale computational problem. Numerical tests show that the proposed algorithm has the same accuracy as the commercial solution software, but has a higher speed. When the demand size is 10, the proposed algorithm can save 24,000 times of time. In addition, 6 reality-based cases are presented, and the results demonstrate that the designed option can save 9.93% of fleet cost, reduce 45.27% of vehicle waiting time, and 33.05% of passenger waiting time relative to other existing customized bus modes when encountering instantaneous passenger flows with time and space imbalance.

Two-Level Planning of Customized Bus Routes Based on Uncertainty Theory

The optimization problem of customized bus routes is affected by uncertain factors in reality; therefore, this paper introduces uncertainty theory to study the above problem. A two-level planning model that takes the maximum total revenue of the bus company as the upper-level goal and the minimum total travel cost of passengers as the lower-level goal is established, using uncertainty theory to study and solve practical problems with uncertain factors. The genetic algorithm is used to solve the model, and the feasibility of the model is verified through a case study. The research results show that the application of the two-level model of customized bus route planning based on uncertain vehicle operating time established in this paper to customize bus route planning can take into account the travel needs of passengers and high-quality experiences while also bringing benefits to enterprises and achieving a win–win situation. The research in this article provides theoretical support for the optimization of customized bus routes.

Identifying the Service Areas and Travel Demand of the Commuter Customized Bus Based on Mobile Phone Signaling Data

In recent years, customized bus (CB), as a complementary form of urban public transport, can reduce residents’ travel costs, alleviate urban traffic congestion, reduce vehicle exhaust emissions, and contribute to the sustainable development of society. At present, customized bus travel demand information collection method is passive. There exist disadvantages such as the amount of information obtained is less, the access method is relatively single, and more potential travel demands cannot be met. This study aims to combine mobile phone signaling data, point of interest (POI) data, and secondary property price data to propose a method for identifying the service areas of commuter CB and travel demand. Firstly, mobile phone signaling data is preprocessed to identify the commuter’s location of employment and residence. Based on this, the time-space potential model for commuter CB is proposed. Secondly, objective factors affecting commuters’ choice to take commuter CB are used as model input variables. Logistic regression models are applied to estimate the probability of the grids being used as commuter CB service areas and the probability of the existence of potential travel demand in the grids and, further, to dig into the time-space distribution characteristics of people with potential demand for CB travel and analyze the distribution of high hotspot service areas. Finally, the analysis is carried out with practical cases and three lines are used as examples. The results show that the operating companies are profitable without government subsidies, which confirms the effectiveness of the method proposed in this paper in practical applications.

Demand Response Mechanism of Customized Bus Based on Space–Time Clustering

Demand Response Mechanism of Customized Bus Based on Space–Time Clustering

Transfer-based customized modular bus system design with passenger-route assignment optimization

Customized bus (CB) is an increasingly popular mode of transportation in many cities around the world. However, studies on CB network design have mostly overlooked three options that may further improve system performance: passenger-route assignment, passenger transfer, and modular vehicles. To bridge this gap, this paper proposes to design a transfer-based CB network with a modular fleet while simultaneously optimizing the passenger-route assignment. To solve the optimal network structure with this new design paradigm, we formulate the network design problem into a nonlinear mixed integer optimization model. A linearization approach and a particle swarm optimization (PSO) algorithm are proposed to solve the exact and near-optimal solution(s) to the model, respectively. Numerical experiments are conducted on the Sioux Falls network and a large-scale network in Chengdu, China. Results show that the customized PSO algorithm efficiently provides high quality near-optimal solutions compared with CPLEX, the genetic algorithm, and the simulated annealing algorithm. Results also show that incorporating passenger-route assignment optimization and the transfer operation produces a more cost-effective CB operational network with less operational costs and higher service quality. The benefit increases as the passenger demand grows.

Customized bus passenger boarding and deboarding planning optimization model with the least number of contacts between passengers during COVID-19

The COVID-19 epidemic has had a major impact on people’s normal travel. Optimizing the control of the number of passengers boarding and deboarding the customized bus (CB) at CB stops can reduce the contact between passengers in the course of travel, which is meaningful for COVID-19 epidemic prevention and control. In this paper, a dynamic programming model based on nonlinear integer programming (NIP) is established to study the problem of boarding and alighting planning at various CB stops under the influence of COVID-19. Using Gurobi 9.1.1 solver, the optimal plan for passengers boarding and deboarding CB buses could be obtained. Besides, the mathematical model established in this paper can obtain the minimum value of the total number of contacts between passengers during travel under different CB numbers. It is found that the model solution results eventually form a Pareto frontier. When the number of CB buses increases, the total number of contacts between passengers will decrease This study has positive significance for ensuring the normal travel of passengers during the COVID-19 epidemic, and provides useful references for the studies about the planning of the customized bus.

Research on Optimization of Customized Bus Routes Based on Uncertainty Theory

In the optimization process of the routes of customized buses, there are numerous uncertainties in the route planning and setting. In this study, the uncertainty theory is introduced into the optimization problem of a customized bus route, and an uncertain customized bus route optimization model is established, which aims at the minimizing the total mileage of vehicle operation. An improved genetic algorithm is used to solve the model, whose feasibility is verified by a case study. The results show that the optimization model based on the uncertainty theory can yield a reasonable customized bus route optimization scheme, and the total mileage reduced from 35.6 kilometers to 32.2 kilometers. This research provides the theoretical support for the optimization of customized bus routes.