Corrigendum to “A Method for Determining Pickup and Delivery Locations of Intercity Customized Bus based on Passenger Demand and POIs” [Multimodal Transp. 2(2026) /100270

This paper describes a research on The Flexible Bus Systems (FBS) using Zigbee as a communication medium. The Flexible Bus System is a demand responsive transit (DRT) but it is more efficient and convenient in a sense that it entertains passenger's demands and gives bus locations in real time. The real time synchronization of The Flexible Bus System makes it information rich and unique as compared to other DRTs. The Flexible Bus Systems is a system that can replace the Traditional Bus Systems with its flexibility and efficiency. This paper discusses the use of wireless technologies in The Flexible Bus Systems and how to make it more reliable using short range wireless technology Zigbee.

In recent decades, the number of vehicles in India has increased enormously, which results in rigorous traffic congestion and pollution in urban areas, especially during peak hours. This paper describes an implementation of Real Time Query System (RTQS) for public transportation service using ZigBee and Radio Frequency Identification (RFID) as a communication medium. The System is a Demand Responsive Transit (DRT) but it is more competent and suitable as it entertains passenger's demands and gives data such as bus locations, bus number, and number of persons inside the bus in real time. The real time synchronization of this system makes this system more informative and distinctive when compared with other DRTs. Moreover, this system is more flexible and efficient when compared with the conventional systems. This paper discusses the usage of wireless technologies in real time query system and how to make it more reliable using wireless technology ZigBee and RFID.

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.

The customized bus (CB) transit is recognized as an effective transportation mode offering more flexible and demand-responsive service than traditional bus transit with fixed route and schedule, especially during the peak hours. The novelty of this study is the development of a mixed integer non-linear model for optimizing multi-terminal CB service in an urban setting. According to the estimated spatiotemporal passenger demand, the objective total cost, consisting of supplier’s and users’ costs, is minimized subject to capacity and time window constraints. A mixed bus fleet with various bus sizes is employed to accommodate passenger demand, which increases vehicle utilization and reduces supplier’s cost. The inconvenience of passengers caused by early arrival at the destination is treated as penalty and considered in users’ cost. The study optimization problem is combinatorial with many decision variables including trip assignment, bus routing and associated timetables, and fleet size. A hybrid genetic algorithm (HGA) which integrates the features of genetic algorithm (GA) and simulated annealing (SA) is developed to effectively search for the optimal solution. A real-world CB network is employed to demonstrate the applicability of the developed model and explore the relation between the model parameters and optimized results. It was found that the total cost can be reduced by 16.5% after employing multiple terminals and a mixed bus fleet.

Assessment of bus speed influencing factors through the exploitation of machine learning techniques

Electric bus transport, a popular mode of public transportation, offers punctual, safe, and comfortable services to passengers through the efficient and effective use of designated road space. The performance of electric bus transport systems depends largely on the design of proper locations of bus stops, with the consideration of passenger demands, waiting time, and traveling time. Optimal electric bus route planning can attract an increasing number of passengers and increase public transit services. Aiming to provide guidance for the electric bus route planning of developing cities, this study proposed an intelligent route planning method to minimize the waiting time and traveling time of passengers, in order to achieve the best comfortable level. In addition, a self‐learning anomaly detection method based on reinforcement learning (RL) was proposed to eliminate abnormal data caused by traffic accidents or emergencies. With a large spatiotemporal dataset collected over 3 years from a real electric bus project in Yantai, China, we developed a prototype system and conducted extensive experiments to evaluate the proposed intelligent route planning method. The results showed that the proposed method can reduce the passengers’ waiting time and attract more passengers traveling by electric bus. In addition, the proposed method has achieved optimal route planning recommendation (RPR) subject to 1,872,391 passenger demands on electric bus services; more than 86% of them were accurately predicted, and more than 97% were satisfied with recommendation results.

Chapter 10 - Bus Arrival Prediction and Trip Planning for Better User Experience and Services

This paper presents a statistical approach to predict the public bus arrival time based on traffic information management system. It considers a number of factors affecting bus travel time, such as departure time, work day, current bus location, number of links, number of intersections, passenger demand at each stop and traffic status of the urban network, etc. A linear model is given to describe the bus arrival time. The parameters of the model are trained by the historical bus arrival times. A prototype system is built to verify the practicability and efficiency of the approach. The approach has been proved relatively accurate and efficient by experiments.

In this work describes web-map public transport enquiry system as described in this paper, gives a good example of providing bilingual information and public transport run in the form of interactive maps and texts, as well as real time derivation of optimal travelling routes for users in terms of multiple criteria, i.e., preferred mode, least changes, shortest travelling time, or lowest fare. This paper describes a research on The Flexible Bus Systems (FBS) using SMAC as a communication medium. The Flexible Bus System is a demand responsive transit (DRT) but it is more efficient and convenient in a sense that it entertains passengers demands and gives bus locations in real time. The real time synchronization of The Flexible Bus System makes it information rich and unique as compared to other DRTs. The Flexible Bus Systems is a system that can replace the Traditional Bus Systems with its flexibility and efficiency. This paper discusses the use of wireless technologies in The Flexible Bus Systems and how to make it more reliable using short range wireless technology SMAC protocol.

A method for determining pickup and delivery locations of intercity customized bus based on passenger demand and POIs

Several developed countries have implemented smart systems for public transport that provide mobility services for citizens. Most of these systems use special infrastructures to determine the location of citizens and public buses. However, the implementation of these systems does not take into account the poor infrastructure of developing countries. In Mexico, Urban Passenger Transport has insufficient transport units to meet the demand of passengers who move throughout the day, and these do not consider hardware infrastructure. Our solution is focused on inexpensive devices that are accessible to citizens, such as mobile phones. In this research work, a smart information system for passengers of urban transport is presented that allows passengers to know the expected arrival times and also to know the availability of seats on the bus that is heading towards their position. The solution was evaluated with real routes, and the results are promising for a pilot project to be put in practice.

As bus passengers often spend substantial amounts of time waiting for buses, many hope for access to real-time transit information to fulfill passenger demands for routes, station location, transit duration, and bus frequency information. This study develops a “Smart Bus Dynamic Transit System” using cellular phones to combine internet, XML documents, GPS positioning and transit path planning models to allow the inquiry of real-time bus and station locations. Users will be able to rapidly obtain knowledge of optimal transit locations and times, as well as estimated arrival times as reflected on Google Map; thus reducing passenger transit time and improving public transportation system efficiency.

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

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

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.