Sustainable Urban Transport System Research Articles

Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China

Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.

Assessment of passenger safety risk level on the Transjakarta public transportation system

Passenger safety is a crucial factor in public transportation, especially now when people prefer using public transport due to its cost-effectiveness compared to private vehicles. This study aims to identify and assess the hazards that can occur in the passenger cabin of a bus and the procedures to be followed in the event of an accident. The assessment of passenger safety levels is conducted using the Hazard and Operability (HAZOP) and Hazard Identification (HIRADC) methods, which are designed to identify and evaluate these potential hazards. This research, after thorough examination and study, aims to establish new protocols that can further enhance passenger safety. With the actions suggested by the author to ward off all these potential hazards, it is hoped that the hazard level will be reduced from extreme to medium and so on. The research study, “Assessment of Passenger Safety Risk on the Transjakarta Public Transportation System,” has the potential to make a substantial contribution to sustainable transportation. In line with sustainable development goals, it immediately tackles safety concerns in the Transjakarta system, improving all-around safety and promoting public transportation over private vehicles. By lowering accident-related expenses, promoting inclusiveness, and enhancing community well-being, the study also indirectly increases resource efficiency. In essence, it contributes to a more secure, egalitarian, and sustainable urban transportation system while advancing Jakarta’s sustainable mobility strategy by placing safety first.

A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China

Urban public transport systems, characterised by their complexity, generate vast data sets that pose challenges to traditional analytical methods. To address this issue, our research introduces an innovative natural feature profile framework, leveraging a comprehensive, data-driven approach that incorporates big data, data mining, machine learning, and correlation analysis. This approach provides detailed insights essential for transport planning and policy development. The framework's core is its three-layered structure: the data layer, the feature layer, and the application layer, complemented by a unique four-level feature tagging system. This system investigates correlations, significance, and sensitivities amongst feature tags. It facilitates the extraction of natural feature profiles from voluminous data sets, rendering the framework highly applicable in practical scenarios. The implementation of this framework in Suzhou and Lianyungang demonstrated its adaptability and effectiveness. The findings underscored distinct city-specific transport patterns, highlighting the necessity for customised transport strategies. Furthermore, our framework excels at capturing spatial–temporal dynamics, offering essential insights grounded in evidence. Overall, this paper introduces a methodical, adaptable, and data-oriented framework, signalling a promising future for the development of intelligent and sustainable urban public transport systems.

How does the urban built environment affect dockless bikesharing-metro integration cycling? Analysis from a nonlinear comprehensive perspective

The multi-modal combination of "dockless bikesharing (DBs) + metro" can effectively addresses the "last mile" challenge, and its importance to improve the quality of metro integration and residents' commuting efficiency is becoming increasingly prominent. As one of the important determining factors of transportation, the urban built environment directly affects the integration travel behavior of residents. To better understand this issue, it is necessary to conduct in-depth research on the influence mechanism of urban built environment on DBs-metro integration cycling (DBsMIC), and reveal its complex nonlinear characteristics and spatial heterogeneity. Therefore, taking Shenzhen as a case study, this study first introduced the OLS regression model to detect the significant built environmental impact factors of DBsMIC. Secondly, the random forest regression model was used to analyze its nonlinear influence. Finally, aiming at the problem of spatial non-stationarity of influencing effects, the spatial heterogeneity is analyzed from both global and local perspectives based on the MGWR model. The results show that the significant impact indices of the built environment of the integration cycling volume vary in different time periods. Overall, seven factors such as road and residential density have a significant impact on DBsMIC. Among them, the job-housing balance and the bus line density have a negative impact, while the rest have a positive impact. Meanwhile, the nonlinear effect of the built environment on DBsMIC is obvious, and there is an obvious marginal effect. In addition, the nonlinear impact of the built environment on DBsMIC in different time periods has spatial heterogeneity and spatial marginal effects. For example, the influence of road density shows a global positive correlation effect in multiple time periods, and the promotion effect increases from southwest to northeast. This study can provide auxiliary decision-making support for the construction of an efficient, green and sustainable urban transportation system according to local conditions.

Evaluation indicators for road traffic energy consumption: a review and prospect.

Indicators for evaluating road traffic energy consumption are critical parameters in the research field of road traffic energy consumption. Improving the applicability of energy consumption indicators can promote the development of green transportation in cities. However, there is currently a lack of systematic analysis of energy consumption indicators in research. Therefore, based on a comprehensive analysis of relevant literature, this study divides the indicators for evaluating road traffic energy consumption into two categories: macro (aimed at traffic systems or traffic flow) and micro (aimed at vehicles). These indicators are subdivided into four categories according to their application characteristics, including general, specific, predictive, and comprehensive. This paper provides a complete summary of various evaluation indicators, including their scope of application, advantages, and limitations, and highlights the relationships between them. Additionally, recommendations are made for the future development of evaluation indicators. Research has found that micro-level general indicators serve as the fundamental components of the mathematical structure for almost all other indicators. Specialized indicators primarily evaluate energy consumption in different driving states of vehicles. Predictive indicators are mainly used for assessing transportation energy consumption in simulation conditions. Comprehensive indicators are mainly applied to evaluate the life cycle energy consumption of vehicles or transportation systems. In future research, the performance of indicators can be improved through the design of standardized indicators, enhancement of energy consumption prediction accuracy, and integration of traffic flow parameters. The research contributes to upgrading energy-saving technologies in road transportation and developing sustainable urban transportation systems.

Will gated community affect public transit service coverage?

The proliferation of gated communities (GC) has prompted researchers worldwide to investigate its causes and consequences. The phenomenon has been extensively discussed as a significant residential form in the world, but most of the studies are primarily concerned with non-technical issues such as inequality and segregation, while technical issues such as urban transit (PT) systems have not been fully discussed. The significance of accessibility-focused PT planning to establish a sustainable urban transportation system makes this issue a crucial topic to explore. PT is a public good expected to be accessible to everyone, and this means the distance between people and the system needs to be minimized to maximize the service coverage, but the GC design is observed to be increasing the distance of its residents willing to use this service. Therefore, this study analyzed the impact of GC on the PT system using Bogor Municipality as a case study through GIS (Geographical Information System) methodologies and spatial data. The quantitative evidence showed that GC has a relatively strong negative correlation with PT service coverage (r (66) = from -0.611 to -0.631, p <.001). This means PT service coverage tends to be low in areas with a high GC ratio and vice versa. It was also discovered that an increase in GC ratio is likely to cause a reduction in PT service coverage in the study area (Beta = -0.48, t(67) = -5.17, p =.000). This article concludes with a discussion of a few potential ideas based on the observations to alleviate the negative impact of GC.

Decision-making for sustainable urban transportation: A statistical exploration of innovative mobility solutions and reduced emissions

This research aims to investigate sustainable urban transportation decision-making through innovative mobility solutions, aiming to curtail emissions. To this end, this research presents a smart stochastic architecture to optimize renewable energy-dependent systems, addressing challenges in networked microgrids. By integrating electric vehicle (EV) charging demands and harnessing the unpredictability of renewable energy resources into the urban networks, the research introduces a vehicle-to-grid layout and a novel machine learning-enabled probabilistic Technique. These advancements cover the way for stable, sustainable, and efficient urban transportation systems with reduced environmental impact. A smart stochastic architecture to optimize the operation and management of such systems that rely heavily on renewable energy is proposed in this study, taking into account the poor reliability and complicated energy management in networked microgrids (MGs). The stochastic and variable charging EVs and also the unpredictability of renewable energy resources (RERs) are incorporated in the suggested layout. A vehicle to grid layout, which has become suitable for the MG cost function, is being used for mitigating adverse impacts of vehicles on MGs. In order to support the use of RERs, this study proposes a new machine learning-enabled probabilistic method utilizing support vector machine and point estimation methods for keeping the system in a stable state taking into account their random behavior. Furthermore, this paper develops a novel optimization algorithm using modified particle swarm optimization to determine what is optimal. IEEE systems evaluate the suitability of the suggested smart layout.

Congestion pricing for sustainable urban transportation systems considering carbon emissions and travel habits

Effective congestion pricing is essential for the sustainable development of urban transportation systems. To explore the impact of congestion pricing on travel behaviors and the urban environment, this paper investigates vehicular carbon emissions and travelers' car dependency under three different schemes of congestion pricing (CP), namely, charging by travel distance (CP1), charging by travel time (CP2), and charging by distance+time (CP3). The interaction between travel behavior adaptation and the corresponding CP scheme is considered in the bi-level model we constructed to determine the optimal rate for the best of regional traffic and carbon emissions. The results of the case study indicate that all schemes perform well in meaningful ways, while CP3 works the best in terms of carbon emission (-23.06 %) and car-mode share (-4.9 %). This suggests that CP3 is a more promising charging mode, as it tends to increase the complexity and psychological pressure for travelers looking for alternatives and inhibiting their car reliance and habitual paths. The findings are conducive to forming the habit of green mode use so as to facilitate carbon emission reduction and traffic jam alleviation, which provide references for policy-making and promote urban transportation systems to operate in an environmentally sustainable and socially responsible manner.

Evolutionary algorithms for dynamic optimization of plug-in charging station networks

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of IoT on urban mobility, using empirical data obtained from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its alignment with actual data. The data reveals a link between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of anticipating congestion and AQI, hence confirming the effectiveness of the models. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban mobility, enhancing decision-making processes, and creating sustainable urban environments via the use of data-driven insights and proactive interventions.

Enabling Sustainable Urban Transportation with Predictive Analytics and IoT

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of Internet of Things (IoT) on urban mobility, using empirical data gathered from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its correspondence with actual data. The data reveals a direct relationship between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of the models in forecasting congestion and AQI, thereby confirming their effectiveness. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban transportation, enabling more intelligent decision-making, and creating sustainable urban environments driven by data-driven insights and proactive actions.

Enhancing Urban Mobility through Traffic Management with UAVs and VLC Technologies

This paper introduces a groundbreaking approach to transform urban mobility by integrating Unmanned Aerial Vehicles (UAVs) and Visible Light Communication (VLC) technologies into traffic management systems within smart cities. With the continued growth of urban populations, the escalating traffic density in large cities poses significant challenges to the daily mobility of citizens, rendering traditional ground-based traffic management methods increasingly inadequate. In this context, UAVs provide a distinctive perspective for real-time traffic monitoring and congestion detection using the YOLO algorithm. Through image capture and processing, UAVs can rapidly identify congested areas and transmit this information to ground-based traffic lights, facilitating dynamic traffic control adjustments. Moreover, VLC establishes a communication link between UAVs and traffic lights that complements existing RF-based solutions, underscoring visible light’s potential as a reliable and energy-efficient communication medium. In addition to integrating UAVs and VLC, we propose a new communication protocol and messaging system for this framework, enhancing its adaptability to varying traffic flows. This research represents a significant stride toward developing more efficient, sustainable, and resilient urban transportation systems.

Long-term urban railway network expansion and pollutant emissions: Methodological approach and insights for sustainable transportation policies

Urban transportation is a major challenge in many cities worldwide and railway systems are often considered as an alternative to solve mobility and air pollution problems. However, such systems are capital-intensive, and their implementation often extends over several years, particularly in developing countries. This paper proposes a methodological approach to assess the impact of long-term urban railway network expansion on pollutant emissions applied to the Metropolitan Region of São Paulo (MRSP) in Brazil. The proposed approach consists of seven steps: define the trips of interest in a database derived from an OD survey; estimate the trip matrix in a baseline scenario and a future scenario considering the expansion of the rail network; determine the trip attributes in both scenarios; calculate the modal choice based on a Logit model; determine the number of trips per mode; estimate vehicle emissions using the bottom-up methodology; and compare the emissions between scenarios. The results of the proposed railway network would lead to a substantial increase in railway ridership between the base scenario of 2017 and the future scenario of 2030, reducing the choices for automobiles by 41%. Nonetheless, the decrease in vehicular emissions would be modest, primarily attributed to reductions in short-distance trips, resulting in estimated reductions of 0.25% for PM, 1.17% for NOx, and 3.93% for CO emissions. These findings underscore the importance of assessing the potential benefits of railway expansion with tools that consider multiple data sources and models, and the need for integrated policies to achieve sustainable urban transportation systems.

Analysis of Traffic Characteristics in Beijing --Based on the Traffic Performance Index

This study aims to deal with the increasingly serious urban traffic congestion problem, taking Beijing as a case study. With the increase in population, traffic congestion has become a serious problem affecting peoples life quality and social and economic development. This study uses the time series analysis and uses the ARIMA (1,0,0) (0,1,1)12 model to forecast the traffic performance index (TPI) data of the past ten years, and deeply analyzes the traffic congestion situation of the next twelve months. The results show that this model can capture the seasonal trend of traffic congestion well, and provide strong decision support for traffic management departments. Through the interpretation and analysis of the forecast, this study gives a series of traffic management suggestions for different months to achieve the alleviation and optimization of urban traffic congestion. In conclusion, this study provides a useful reference for urban traffic management and planning and contributes to the construction of a more efficient, convenient, and sustainable urban transportation system.

Unveiling the roles of public bike systems: From leisure to multimodal transportation

As bike-sharing systems gain popularity, understanding their roles in urban transportation, such as the first/last mile problem and the replacement of public transit, is crucial to comprehensively promote urban transport systems. Due to the Internet of Things (IoT) technique, copious data from public bike trips worldwide are available to urban researchers. In this study, we examine how public bikes function not just as a means of transportation, but also for leisure. The leisure function was ignored in previous empirical studies using data mining techniques, an oversight that could skew results in places like Seoul where leisure trips constitute a significant portion of the trips. We investigate when, where, and to what degree bike-sharing serves as a leisure tool and substitutes for, integrates with, or complements the public transit system. We propose an integrated data-mining approach to identify the above four categories of public bike trips. Our method combines multiple sources of real-world data, the distributed density-based spatial clustering of applications with noise (DBSCAN), and spatial feature engineering. The results reveal that the distribution of each category shows substantial spatial–temporal heterogeneity. Longitudinal analysis reveals that the use of public bicycles as a transportation mode primarily contributes to the growth observed from 2018 to 2021, compared to use for leisure. The determining factors and non-linear functions of each type of bike trip were interpreted by explainable machine learning methods, Shapley additive explanations (SHAP), and partial dependence plot (PDP). We discovered that lower slopes, higher residential densities, and more commercial neighborhoods consistently attract users across all categories. Additionally, rivers and green areas attract more leisure trips, while areas with denser transit routes see more substitution trips and fewer complementary trips. By understanding the characteristics, spatial–temporal distribution, and determining factors of public bike trips across these four categories, city planners and operators can tailor services to meet the diverse needs of citizens, fostering a more convenient, sustainable, and accessible urban transport system.

Building A Penta Helix Collaboration Model On Mass Rapid Transit Jakarta

The population in Jakarta as a metropolis continues to increase along with the growing phenomenon of urbanization, which impacts increasing population mobility. The Jakarta Mass Rapid Transit (MRT) public transportation policy is significant in addressing urban mobility challenges and improving efficient transportation systems. This study aims to analyze the collaboration of the five pillars of penta helix on the Jakarta Mass Rapid Transit (MRT), both in government, companies, academics, media, and the environment in the development of the Jakarta MRT. The research used a qualitative method with a descriptive and exploratory design approach from January to August 2023. This study uses data collection techniques through in-depth interviews, observations, literature reviews, and policy analysis related to the Jakarta MRT. The results showed that the Penta Helix model is a framework for developing the Jakarta MRT. Close collaboration between government, companies, academics, the environment, and the media is needed to achieve effective and sustainable MRT services. Practical cooperation between the helixes is critical to overcoming obstacles in the Jakarta MRT system. The Jakarta MRT can contribute more to an efficient and sustainable urban transportation system. The study recommends that the company facilitate cross-sector cooperation, increase community participation, and adopt the latest technological solutions in the development and sustainable operation of the Jakarta MRT.

Artificial Intelligence-Based Real-Time Traffic Management

The paper proposes applying the ant colony optimization algorithm within a distributed multi-agent architecture, leveraging IoT technology, to address path routing challenges in urban traffic. The study emphasizes the potential of advanced AI techniques and multi-agent systems in revolutionizing traffic management for efficient and sustainable urban transportation systems. Our study addresses the challenging issue of traffic congestion in modern urban areas and the limitations of traditional solutions like road expansion and network indicators. To effectively tackle traffic congestion, the study explores various strategies that analyze traffic elements, falling into the Macroscopic and Microscopic Models. However, conventional traffic modeling faces significant challenges in dealing with complex traffic systems. The rise of the Internet of Things (IoT) offers opportunities for effective traffic analysis by collecting vast and uncertain data. Nevertheless, most existing systems focus on local events, leaving a gap in their effectiveness. To address this, multi-agent systems become a novel strategy, deploying distributed agents across road intersections for comprehensive traffic management. Artificial Intelligence (AI) techniques, including fuzzy logic, evolutionary algorithms, neural networks, and reinforcement learning, emerge as promising solutions for traffic control. For instance, artificial neural networks accurately predict urban traffic flow, and meta-heuristic AI approaches like the artificial bee colony algorithm optimize signal timings.

Development of micromodels of traffic flows, taking into account environmental factors

The article considers an actual problem of organizing a safe and sustainable urban transport system. We have examined the existing positive global experience in both infrastructural and managerial decisions. Then to assess possible solutions at the stage of infrastructure design, we have developed the simulation micromodels of transport network sections of the medium-sized city (Naberezhnye Chelny) with a rectangular building type. The models make it possible to determine the optimal parameters of the traffic flow, under which pollutant emissions from cars would not lead to high concentrations of pollutants. Also, the model allows to obtain the calculated values of the volume of emissions of pollutants and the parameters of the traffic flow (speed, time of passage of the section, etc.). On specific examples, the proposed method’s effectiveness is shown. Case studies of cities of different sizes and layouts are implementation examples and possible uses proposed by the models. This study has shown the rationality of the suggested solution at the stage of assessing infrastructure projects and choosing the best option for sustainable transport development. The proposed research method is universal and can be applied in any city.

Strategies and Policy Frameworks for Low Carbon Mobility in Indian Cities

This paper endeavours to illuminate the challenges and issues that need to be addressed to achieve sustainable urban transportation system in Indian cities. This paper will try to trace pattern of urbanization, motorization and their impact on mobility, environment, health and road safety. Further, the paper will explain various efforts taken by government authorities to address the transportation issues and their outcomes. The paper also explains various strategies which if be adopted will ensure low carbon growth of urban areas. Low carbon cities will help in enhancing India's energy security and lower its carbon footprint.

Optimizing Traffic Control with Real-time Data: A HERE API-Based Approach

Traffic congestion is a pervasive issue in urban areas, leading to increased commute times, environmental pollution, and reduced overall quality of life. To address this challenge, this paper introduces a dynamic traffic control system that leverages the HERE API for real-time traffic data. The system provides a web- based interface with user authentication and allows users to input the address of a signal they wish to control. Utilizing the HERE API, it fetches live traffic information, including jam factor and free flow speed, for all four roads at the specified signal. This data serves as the foundation for an intelligent traffic management system that dynamically allocates green signal times based on the intensity of traffic, with roads experiencing higher congestion receiving longer green time. The system's backend is built using Python and the Flask framework, while the frontend features HTML and CSS for a user-friendly interface. Authentication ensures that only authorized users can access and utilize the system. Geocoding is employed to convert user-provided addresses into geographic coordinates, enabling precise data retrieval from the HERE API. Once the traffic data is collected, an analysis is performed to identify the road with the highest traffic intensity. The dynamic allocation of green signal times is a fundamental feature, allowing for real-time traffic optimization and a reduction in congestion. The paper explores the development, implementation, and testing of this dynamic traffic control system, emphasizing its potential to significantly improve urban mobility. Furthermore, considerations for scalability, security, and maintenance are addressed to ensure the system's practicality in real-world scenarios. This project contributes to the growing field of intelligent transportation systems by showcasing the benefits of data-driven decision-making in traffic signal control. By utilizing the HERE API, this system not only provides a solution to mitigate traffic congestion but also represents a step towards more sustainable and efficient urban transportation systems. Key Words: Traffic control, HERE API, Dynamic signal allocation, Real-time traffic data, Urban mobility, Traffic congestion, Traffic optimization, Intelligent transportation systems, Geocoding, User authentication, Python backend, Flask framework, User interface, Green signal time, Scalability, Security, Maintenance, Sustainable transportation.

How do dockless bikesharing services affect public transit and taxi use? Evidence from 36 central cities in China

Despite the rapid growth in the popularity of technology-enabled dockless bikesharing services, comprehensive research on their causal effects on other modes of transportation is still scarce. To fill this gap, this study exploits the staggered entry of Ofo and Mobike into 36 Chinese central cities as a quasi-natural experiment using a difference-in-differences approach to examine the overall impact of dockless bikesharing services on bus, rail, and taxi use. The results show that the entry of dockless bikesharing services significantly reduced city bus ridership by 5.1% and taxi passenger miles by 6.4%, but had no significant impact on rail ridership overall. Further heterogeneity analysis shows that a higher proportion of BRT vehicles can attenuate the negative impact of bikesharing services on bus ridership, while bikesharing seems to promote rail transit use in cities with low population densities and limited rail transit networks. Moreover, the reduction effect of bikesharing services on taxi passenger miles was driven by a decrease in average trip distance, with the unintended consequence of total taxi deadheading increased by 9.7%. This study provides important and valuable references for city governments to better utilize bikesharing services to develop efficient and sustainable urban transportation systems.