Urban Bus Network Research Articles

Optimizing urban bus network based on spatial matching patterns for sustainable transportation: A case study in Harbin, China.

The rapid economic development and accelerating urbanization have led to a significant mismatch between the urban bus network allocation and the population flow. Therefore, this paper investigates this challenge by exploring the intricate relationship between the population flow dynamics, traffic congestion conditions, and the efficient allocation of bus resources. In response, two key indexes were introduced based on spatial matching patterns to assess the urban bus network: the Population-Bus Match Index evaluates the matching degree between supply and demand, and the Population-Congestion Match Index evaluates the matching degree between utilization and saturation. Additionally, two distinct optimization strategies have been proposed to enhance the urban bus network. The first optimization strategy considers the bus network's current status, while the second aspires to an idealized scenario. Subsequently, the potential contributions of each bus station in reducing CO2 emission reduction after implementing the two optimization strategies are quantified. Utilizing a case study focused on Harbin, the proposed methods are validated. The findings unveil a substantial misalignment between supply and demand within the bus network during peak periods, with nearly half of the bus stations experiencing a disparity between utilization and saturation. Comparative experiments across different optimization strategies reveal that the second optimization strategy significantly outperforms the first, but the first optimization strategy has a higher degree of CO2 emission reduction contribution. The results of this study provide decision-makers with an environmentally oriented vantage point for the discerning selection of optimization strategies and leave valuable insights for urban areas confronting transportation challenges.

Optimizing quasi-dynamic wireless charging for urban electric buses: A two-scenario mathematical framework with grid and PV-battery systems

This paper presents a mathematical optimization framework for the strategic placement of quasi-dynamic wireless charging (QWC) stations within road networks to address the charging needs of battery electric buses (BEBs). This study evaluates two scenarios for powering the buses. In the first scenario, a grid-connected system is considered. The optimization aims to minimize annual costs related to capital, operation, and energy losses of the electric bus fleet. This involves determining the optimal locations for QWC stations, the length of power transmitters, and the corresponding battery capacities for the BEBs. Using MATLAB-based optimization tools Casadi and Yalmip, with solvers Bonmin and Fmincon, the optimal configuration includes a 13 kWh battery capacity and a 300 m power transmitter distributed across five bus stop areas. The second scenario employs a chance-constrained optimization approach for an isolated solar photovoltaic (PV) and battery energy storage system (BESS). This system is designed to reliably meet the BEBs' energy requirements throughout the day, considering different seasonal data (winter, summer, all seasons/year-round). The optimization results for the PV and BESS capacities vary with the seasons: 394.247 kW and 2012.6 kWh using summer data, 1762.1 kW and 2738.2 kWh using winter data, and 1610.8 kW and 2741.9 kWh using year-round data. Additionally, the paper examines the impact of varying bus fleet sizes on the optimal battery size and power transmitter combination using a real-world example of the bus route between Khalifa City and Abu Dhabi Downtown in the UAE. The findings suggest that larger batteries with fewer or no charging stations are more economical for smaller fleets. Conversely, as the fleet size increases, a combination of smaller battery sizes and a greater number (and length) of QWC (power transmitters) becomes more cost-effective. This research offers significant insights into the efficient deployment of QWC stations and the integration of renewable energy and energy storage for sustainable urban electric bus networks. The proposed optimization models provide a systematic approach to designing and operating charging infrastructure, contributing to sustainable urban transportation systems. Moreover, the study highlights the influence of seasonal data on PV system sizing and costs.

Considering the Optimization Design of Urban Bus Network Scheduling

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%.

Vulnerability analysis of public transit systems from the perspective of the traffic situation

The critical to sustainable urban development that a robust bus transit system is constructed to alleviate urban congestion problems. Unfortunately, disruptions in bus transit systems can lead to dysfunction and threaten sustainable development. This paper proposed a vulnerability analysis model for constructing complementary urban bus networks (UBNs), which considers the actual capacity of the bus network at the speed of transport roads. In addition, the UBNs aimed analysis method is presented from three specific aspects: (a) utilizing the stochastic user equilibrium model and different traffic situations to measure the UBNs of passenger flow distribution according to multi-source data; (b) introducing the node capacity and structural, functional vulnerability analysis model by combining real traffic flows and the complex network theory; (c) analysing the vulnerability characteristics of UBNs cascading failures from different passenger flow distributions and increasing capacity parameters, and comparing with those under different attack methods. Then, the new vulnerability analysis model is applied to the Harbin trunk bus. Considering different attack scenarios, the UBNs show strong vulnerability in the case of high congestion. Compared with other deliberate attacks, the bus network based on the attack mode of the maximum intensity site is more vulnerable at different times. These insights could be used to inform a vulnerability-based spatiotemporal design of bus transit networks.

An Urban Bus Network Generation Algorithm Based on Particle Swarm Optimization and Force Field Properties

Due to continuous urban sprawl, large-scale bus network design has become a major challenge in urban transport planning. The continuous increase in urban population and scale makes the factors considered in the urban route network design increasingly complex. Contemporary public transportation network design problems are based more on efficiency goals such as the accessibility and comfort of the transportation network, which increases the difficulty of analyzing the problem. Bus network design is not only an NP-hard (nondeterministic polynomial) problem but also a multivariable and multiobjective problem. This paper focuses on the bivariate and multiobjective bus network design problem of route generation and station selection. This paper proposes an algorithm called the Pseudo Force Field. By combining the idea of Particle Swarm Optimization (PSO) and the properties of the force field, a feasible route generation scheme is provided for the design of the bus network. The algorithm does not need to determine the end station and has a high degree of completion of the demand. This solves the problem of the selection of terminal stations in large-scale road network design. On this basis, the article combines Genetic Algorithm (GA) and Pareto frontier to provide a new route optimization algorithm and proves the effectiveness of the algorithm. The model has achieved theoretical results in the design of the bus route network in the megacity of Shenzhen, China.

The External Characteristics and Mechanism of Urban Road Corridors to Agglomeration: Case Study for Guangzhou, China

Existing research on the agglomeration effect of urban roads mainly focuses on land use but ignores the differences between various locations, types, and directions of roads. Few studies have been conducted on the built buildings which can represent the actual utility, and land use as a kind of government authorization may not necessarily represent actual needs. This research provides an analytical framework and an empirical analysis to study the differences in impacts of different urban roads on land use and to identify its internal dynamic mechanism. Guangzhou, being the research object, is one of the five major central cities in China. By using the techniques of GIS and SPSS, together with the methods of corridor effect, correlation analysis, and geographic detector, we analyze the external characteristics of office buildings and land gathering along both sides of the roads, explore the urban characteristics of corridor effect, then analyze the relationship with urban traffic flow and bus network density in order to find out the internal motivation of corridor effect. The fundamental conclusion can be drawn that the corridor effect on the land used for commercial offices is mostly unnoticeable, and roads of different locations, types, and directions display various scope and intensity of corridor effects. The agglomeration power is mainly caused by private transportation and has no relationship with public transport. The article concludes the model of the corridor effect and provides some policy suggestions to the government in order to strengthen the linkage development of transportation and land and to promote the improvement of land use efficiency.

An Improved Ant Colony Algorithm for Urban Bus Network Optimization Based on Existing Bus Routes

Adding new lines on the basis of the existing public transport network is an important way to improve public transport operation networks and the quality of urban public transport service. Aiming at the problem that existing routes are rarely considered in the previous research on public transportation network planning, a public transportation network optimization method based on an ant colony optimization (ACO) algorithm coupled with the existing routes is proposed. First, the actual road network and existing bus lines were abstracted with a graph data structure, and the integration with origin–destination passenger flow data was completed. Second, according to the ACO algorithm, combined with the existing line structure constraints and ant transfer rules at adjacent nodes, new bus-line planning was realized. Finally, according to the change of direct passenger flow in the entire network, the optimal bus-line network optimization scheme was determined. In the process of node transfer calculation, the algorithm adopts the Softmax strategy to realize path diversity and increase the path search range, while avoiding premature convergence and falling into local optimization. Moreover, the elite ant strategy increases the pheromone release on the current optimal path and accelerates the convergence of the algorithm. Based on existing road network and bus lines, the algorithm carries out new line planning, which increases the rationality and practical feasibility of the new bus-line structure.

On the integration of battery electric buses into urban bus networks

Cities all around the world struggle with urban air quality due to transportation related emissions. In public transport networks, replacing internal combustion engine buses by electric buses provides an opportunity to improve air quality. Hence, many bus network operators currently ask for an optimal transformation plan to integrate battery electric buses into their fleet. Ideally, this plan also considers the installation of necessary charging infrastructure to ensure a fleet’s operational feasibility. Against this background, we introduce an integrated modeling approach to determine a cost-optimal, long-term, multi-period transformation plan for integrating battery electric buses into urban bus networks. Our model connects central strategic and operational decisions. We minimize total cost of ownership and analyze potential reductions of nitrogen oxide emissions. Our results base on a case study of a real-world bus network and show that a comprehensive integration of battery electric buses is feasible and economically beneficial. By analyzing the impact of battery capacities and charging power on the optimal fleet transformation, we show that medium-power charging facilities combined with medium-capacity batteries are superior to networks with low-power or high-power charging facilities.

Analysis of Complex Network Characteristics of Bus Stations in City: A Case Study of the Changsha-Zhuzhou-Xiangtan Urban Agglomeration

Based on the current situation and operation of urban bus network stops, this paper uses complex network theory to analyse the topology of the constructed network. Taking the ChangZhuTan bus network as an example, the Space-L method is selected to establish a network topology model, and the topological characteristics of different networks are analysed by calculating the static indicators of the network (degree, degree distribution, average path length, clustering coefficient, etc.). The research results show that the ChangZhuTan bus network has scale-free network characteristics, but the small-world characteristics are not obvious, and the results of the indicator calculation are consistent, which provides a reference for urban transport The results show that the ChangZhuTan bus network has scale-free network characteristics, but the small-world characteristics are not obvious, and the results are consistent with the index calculation, which puts forward reference suggestions for urban transportation planning and development.

Bus journey simulation to develop public transport predictive algorithms

Encouraging the use of public transport is essential to combat congestion and pollution in an urban environment. To achieve this, the reliability of arrival time prediction should be improved as this is one area of improvement frequently requested by passengers. The development of accurate predictive algorithms requires good quality data, which is often not available. Here we demonstrate a method to synthesise data using a reference curve approach derived from very limited real world data without reliable ground truth. This approach allows the controlled introduction of artefacts and noise to simulate their impact on prediction accuracy. To illustrate these impacts, a recurrent neural network next-step prediction is used to compare different scenarios in two different UK cities. The results show that a realistic data synthesis is possible, allowing for controlled testing of predictive algorithms. It also highlights the importance of reliable data transmission to gain such data from real world sources. Our main contribution is the demonstration of a synthetic data generator for public transport data, which can be used to compensate for low data quality. We further show that this data generator can be used to develop and enhance predictive algorithms in the context of urban bus networks if high-quality data is limited, by mixing synthetic and real data. • Method to generate synthetic bus journeys based on very limited and unreliable data. • Insights into data quality challenges in public transport systems. • Algorithms can be improved by training on a mixture of synthetic and real-world data. • The demonstrated data synthesis could be the base for a public transport benchmarking dataset.

A New School Bus Routing Problem Considering Gender Separation, Special Students and Mix Loading: A Genetic Algorithm Approach

In developing countries, whereas the urban bus network is a major part of public transportation system, it is necessary to try to find the best design and routing for bus network. Optimum design of school bus routes is very important. Non-optimal solutions for this problem may increase traveling time, fuel consumption, and depreciation rate of the fleet. A new bus routing problem is presented in this study. A multi-objective mixed integer model is proposed to handle the associated problem. Minimization of transportation cost as well as traveling time is the main objectives. The main contributions of this paper are considering gender separation as well as mixed-loading properties in the school bus routing problem. Moreover, special and handicapped students are considered in this problem. The proposed model is applied in a real case study including 4 schools in Tehran. The results indicate the efficiency of the proposed model in comparison with the existing system. This comparison shows that the students’ travelling time is reduced by 28% for Peyvand middle smart school, 24% for Tehran international school, 13% for Hemmat School and 21% for Nikan High school. A customized Genetic Algorithm (GA) is proposed to solve the model. Penalty functions are used to handle the several constraints of the problem in Genetic Algorithm. The results justify the applicability and efficacy of the both proposed model and solution approach.

Quantification and control of disruption propagation in multi-level public transport networks

Due to the multi-level nature of public transport networks, disruption impacts may spill-over beyond the primary effects occurring at the disrupted network level. During a public transport disruption, it is therefore important to quantify and control the disruption impacts for the total public transport network, instead of delimiting the analysis of their impacts to the public transport network level where this particular disruption occurs. We propose a modelling framework to quantify disruption impact propagation from the train network to the urban tram or bus network. This framework combines an optimisation-based train rescheduling model and a simulation-based dynamic public transport assignment model in an iterative procedure. The iterative process allows devising train schedules that take into account their impact on passenger flow re-distribution and related delays. Our study results in a framework which can improve public transport contingency plans on a strategic and tactical level in response to short- to medium-lasting public transport disruptions, by incorporating how the passenger impact of a train network disruption propagates to the urban network level. Furthermore, this framework allows for a more complete quantification of disruption costs, including their spilled-over impacts, retrospectively. We illustrate the successful implementation of our framework to a multi-level case study network in the Netherlands.

Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect

In recent years, the increasing segregation and fragmentation of transport service provisions, evidenced between urban and rural areas, and between different administrative divisions, has been concerned in metropolitan areas. The analysis of the spatial configuration of urban bus networks is important to understand the spatial problems and relevant mitigation solutions. Previous studies have regarded the station as the nodes in bus network analyses, while few studies have constructed bus lines as nodes through the complex network analysis. In this study, we highlighted the value of bus lines and their network types for analyzing urban bus network pattern. We developed a novel ‘line–line’ network to examine the spatial characteristics of cross-administration bus lines in a case study of Nanjing metropolitan area, China. The results suggest that the bus network exhibited a consistently bimodal node distribution in both line-line and line-node network types, though their internal mechanisms are different. By evaluating the importance of network nodes, administrative divisions were found to impact the bus network integration in the metropolitan area negatively. Community detection results further revealed that the geography of some subnetworks was affected by administrative divisions, with some exhibiting complex spatial characteristics. These insights would inform strategic development policy to improve the spatial equity and efficiency of public transport provisions in urban China.

Resilience Evaluation of Urban Bus-Subway Traffic Networks for Potential Applications in IoT-Based Smart Transportation

In megacities, Internet of Things (IoT) based smart transportation is an effective solution to dealing with traffic congestions. Resilience evaluation of traffic networks can help enhance the management of IoT-based smart transportation. Currently, most of the research work on resilience evaluation is devoted to a single type of traffic network. However, in reality, urban traffic networks are complex, and various types of traffic networks are often highly interrelated and need to be evaluated concurrently. To address the above problem, this paper proposed a method for resilience evaluation of urban bus-subway traffic networks for potential applications in IoT-based smart transportation in megacities. The essential idea behind the proposed method is to combine the urban bus network with the subway network and present a model of an urban bus-subway hybrid traffic network, rather than a single type of traffic network. In the proposed method, (1) an urban bus-subway hybrid traffic networks model is established; (2) four metrics such as the degree centrality and clustering coefficient are used to identify vital nodes in the hybrid traffic network; and (3) four indicators such as the network efficiency and sensitivity are used to analyze the network resilience. The effectiveness of the proposed method is verified by the application of urban bus-subway hybrid traffic networks in Beijing. The proposed method can be potentially applied to optimize the resource allocation of important nodes in an urban hybrid traffic network, which is of great interest for enhancing the management of IoT-based smart transportation in megacities.

Evacuating metro passengers via the urban bus system under uncertain disruption recovery time and heterogeneous risk-taking behaviour

The bus-bridging service has always faced problems in severe emergencies or catastrophes that require the large-scale evacuation of passengers. This paper provides an alternative evacuation scheme which uses the urban bus network in the case of common metro service disruptions; this is modeled by minimizing the total cost of the affected metro passengers, through jointly selecting the bus lines and frequencies. The uncertain recovery time of the service disruption and the heterogeneous risk-taking behavior of the affected metro passengers are incorporated in the scheme. Therefore, we build a linkage between the evacuation service design and the risk-taking behavior of passengers. A heuristic algorithm is proposed to calculate the optimal evacuation scheme. A numerical experiment using a real-world network is conducted to illustrate the validity of the model and algorithm.

Using Open Big Data to Build and Analyze Urban Bus Network Models within and across Administrations

Urban bus networks play an important role, when the capacity of urban public services is evaluated. With recent advancements in Internet and Communication Technologies, there is an emerging interest in building an urban bus network model through open big data. This has rarely been investigated and exposes several challenges in the provision of transportation services in urban planning. On the one hand, it is necessary to combine bus stations based on spatial distance constraints due to their ambiguous definition in open big data; on the other hand, it is difficult and time-consuming to relocate and build new stations, but the optimization of bus lines is relatively easy to implement. This study aimed to develop an explicit methodological framework for building and analyzing two different types of urban bus network model using open big data. Thereafter, the framework was applied in two case studies in China, within a county-level administration and in a region including three county-level administrations. The key result shows that there was a shortage of urban bus services across these different administrations. This paper contributes to the body of research methodologies into public transport networks and to understanding the sharing of urban public services across administrations, improving the management of urban bus networks, and highlighting the importance of examining the characteristics of urban bus network in county-level administrations rather than just in large cities in China.

Exploring the robustness of urban bus network: A case from Southern China

The robustness of urban bus network is essential to a city that heavily relies on buses as its main transportation solution. In this paper, the urban bus network has been modeled as a directed and space L network, and Changsha, a transportation hub of nearly 8 million people and hundreds of bus lines in southern China, is taken as a case. Based on the quantitative analyses of the topological properties, it is found that Changsha urban bus network is a scale-free network, not a small-world network. To evaluate the robustness of the network, five scenarios of network failure are simulated, including a random failure and four types of intentional attacks that differed in key node identification methods (i.e., unweighted degree or betweenness centrality) and attack strategies (i.e., normal or cascading attack). It is revealed that intentional attacks are more destructive than a random failure, and cascading attacks are more disruptive than normal attacks in the urban bus network. In addition, the key nodes identification methods are found to play a critical role in the robustness of the urban bus network. Specifically, cascading attack could be more disruptive when the betweenness centrality is used to identify key nodes; in contrast, normal attack could be more disruptive when the unweighted degree is used to identify key nodes. Our results could provide reference for risk management of urban bus network.

Exploring the Characteristics of an Intra-Urban Bus Service Network: A Case Study of Shenzhen, China

The urban bus service system is one of the most important components of a public transport system. Thus, exploring the spatial configuration of the urban bus service system promotes an understanding of the quality of bus services. Such an understanding is of great importance to urban transport planning and policy making. In this study, we investigated the spatial characteristics of an urban bus service system by using the complex network approach. First, a three-step workflow was developed to collect a bus operating dataset from a public website. Then, we utilized the P-space method to represent the bus service network by connecting all bus stop pairs along each bus line. With the constructed bus network, a set of network analysis indicators were calculated to quantify the role of nodes in the network. A case study of Shenzhen, China was implemented to understand the statistical properties and spatial characteristics of the urban bus network configuration. The empirical findings can provide insights into the statistical laws and distinct convenient areas in a bus service network, and consequently aid in optimizing the allocation of bus stops and routes.

Energy-aware predictive control for electrified bus networks

For an urban bus network to operate efficiently, conflicting objectives have to be considered: providing sufficient service quality while keeping energy consumption low. The paper focuses on energy efficient operation of bus lines, where bus stops are densely placed, and buses operate frequently with possibility of bunching. The proposed decentralized, bus fleet control solution aims to combine four conflicting goals incorporated into a multi-objective, nonlinear cost function. The multi-objective optimization is solved under a receding horizon model predictive framework. The four conflicting objectives are as follows. One is ensuring periodicity of headways by watching leading and following vehicles i.e. eliminating bus bunching. Equal headways are only a necessary condition for keeping a static, predefined, periodic timetable. The second objective is timetable tracking. A conflicting objective to the former is minimizing passenger waiting time. When more than the expected passengers are waiting for the bus it is desirable to haste the vehicle in order to prevent bunching. The final objective is energy efficiency. To this end, an energy consumption model is formulated considering battery electric vehicles with recuperation during braking. Different weighting strategies are compared and evaluated through realistic scenarios, realized in a validated microscopic traffic simulation environment. Simulation results suggest 3–8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses.

Fuzzy Evaluation Method for Comprehensive Performance of Urban Public Traffic Networks

Aiming at the comprehensive performance evaluation of urban public transport network, this paper established an evaluation index system of urban public transport network with target layer, criterion layer and index layer based on analyzing its subjective and objective influencing factors. Otherwise, a method for determining the weight of the comprehensive performance evaluation index of the city bus network and its evaluation model were presented by using fuzzy mathematics principle. The validity and feasibility of the model and method are further validated by the evaluation of the comprehensive performance of the existing bus network in Lanzhou New District. The evaluation results provide theoretical support for the further optimization of urban bus network.