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Yanjie Ji
School of Transportation, Southeast University, Nanjing, China

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Journal article
Published: 23 April 2021 in Information
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In order to solve the oversupply and repositioning problems of bike-sharing, this paper proposes an optimization model to obtain a reasonable supply volume scheme for bike-sharing and infrastructure configuration planning. The optimization model is constrained by the demand for bike-sharing, urban traffic carrying capacity (road network and parking facilities carrying capacities), and the flow conservation of shared bikes in each traffic analysis zone. The model was formulated through mixed-integer programming with the aim of minimizing the total costs for users and bike-sharing enterprises (including the travel cost of users, production and maintenance costs of shared bikes, and repositioning costs). CPLEX was used to obtain the optimal solution for the model. Then, the optimization model was applied to 183 traffic analysis zones in Nanjing, China. The results showed that not only were user demands met, but the load ratios of the road network and parking facilities with respect to bike-sharing in each traffic zone were all decreased to lower than 1.0 after the optimization, which established the rationality and effectiveness of the optimization results.

ACS Style

Jiajie Yu; Yanjie Ji; Chenyu Yi; Chenchen Kuai; Dmitry Samal. Optimization Model for the Supply Volume of Bike-Sharing: Case Study in Nanjing, China. Information 2021, 12, 182 .

AMA Style

Jiajie Yu, Yanjie Ji, Chenyu Yi, Chenchen Kuai, Dmitry Samal. Optimization Model for the Supply Volume of Bike-Sharing: Case Study in Nanjing, China. Information. 2021; 12 (5):182.

Chicago/Turabian Style

Jiajie Yu; Yanjie Ji; Chenyu Yi; Chenchen Kuai; Dmitry Samal. 2021. "Optimization Model for the Supply Volume of Bike-Sharing: Case Study in Nanjing, China." Information 12, no. 5: 182.

Article
Published: 15 April 2021 in Journal of Central South University
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As the demand for bike-sharing has been increasing, the oversupply problem of bike-sharing has occurred, which leads to the waste of resources and disturbance of the urban environment. In order to regulate the supply volume of bike-sharing reasonably, an estimating model was proposed to quantify the urban carrying capacity (UCC) for bike-sharing through the demand data. In this way, the maximum supply volume of bike-sharing that a city can accommodate can be obtained. The UCC on bike-sharing is reflected in the road network carrying capacity (RNCC) and parking facilities’ carrying capacity (PFCC). The space-time consumption method and density-based spatial clustering of application with noise (DBSCAN) algorithm were used to explore the RNCC and PFCC for bike-sharing. Combined with the users’ demand, the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users’ demand, so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly. The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing, China. Compared with the field survey data, the effect of the proposed algorithm was verified.

ACS Style

Jia-Jie Yu; Yan-Jie Ji; Chen-Yu Yi; Yang Liu. Estimating model for urban carrying capacity on bike-sharing. Journal of Central South University 2021, 1 -11.

AMA Style

Jia-Jie Yu, Yan-Jie Ji, Chen-Yu Yi, Yang Liu. Estimating model for urban carrying capacity on bike-sharing. Journal of Central South University. 2021; ():1-11.

Chicago/Turabian Style

Jia-Jie Yu; Yan-Jie Ji; Chen-Yu Yi; Yang Liu. 2021. "Estimating model for urban carrying capacity on bike-sharing." Journal of Central South University , no. : 1-11.

Research article
Published: 15 February 2021 in Discrete Dynamics in Nature and Society
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How to meet the daily demand for resident transport while limiting the transmission of infectious diseases is a problem of social responsibility of urban transport systems during major public health emergencies. Considering the novel coronavirus pneumonia epidemic (COVID-19), a bus timetable system based on the “if early, wait, and if late, leave soon” strategy is proposed. Based on public transport vehicle constraints in this system, the concept of reliability is introduced to evaluate public transport timetable systems, and a model based on an event tree is built to calculate the failure rate of urban bus timetables. Then, the public transport situation in Yixing city is used as an example to perform confirmatory analysis, and the fluctuations in the reliability of the bus timetable during the novel coronavirus pneumonia epidemic are discussed. The research results show that the method proposed in this paper can obtain the overall failure rate of urban bus timetable by traversing the calculation of each round-trip interval and achieve an accurate evaluation of the reliability of bus timetables. During the early, middle, and more recent stages of the COVID-19 outbreak, the failure rate of bus timetables in Yixing city initially decreased and then increased. In the early stage of the outbreak, the failure rate of the Yixing bus timetable was 7.8142. However, the failure rate decreased to 4.3306 and 5.0160 in the middle and late stages of the epidemic, respectively. In other words, the failure rate of the public transport network in the middle and late stages decreased by 44.58% and 35.81%, respectively, compared with that in the early stage. Thus, during major health emergencies, such as the novel coronavirus pneumonia outbreak, the reliability of the urban bus timetable system can be improved by at least 35%, and cross-infection at bus stations can be prevented. The research results verify the feasibility and reliability of the implementation of bus timetabling strategies during major health emergencies.

ACS Style

Liangpeng Gao; Yue Zheng; Yanjie Ji; Chenghong Fu; Lihai Zhang. Reliability Analysis of Bus Timetabling Strategy during the COVID-19 Epidemic: A Case Study of Yixing, China. Discrete Dynamics in Nature and Society 2021, 2021, 1 -14.

AMA Style

Liangpeng Gao, Yue Zheng, Yanjie Ji, Chenghong Fu, Lihai Zhang. Reliability Analysis of Bus Timetabling Strategy during the COVID-19 Epidemic: A Case Study of Yixing, China. Discrete Dynamics in Nature and Society. 2021; 2021 ():1-14.

Chicago/Turabian Style

Liangpeng Gao; Yue Zheng; Yanjie Ji; Chenghong Fu; Lihai Zhang. 2021. "Reliability Analysis of Bus Timetabling Strategy during the COVID-19 Epidemic: A Case Study of Yixing, China." Discrete Dynamics in Nature and Society 2021, no. : 1-14.

Journal article
Published: 06 February 2021 in Accident Analysis & Prevention
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The red-light running (RLR) behavior of delivery e-bike (DEB) riders in cities has become the primary cause of traffic accidents associated with this group at signalized intersections. This study aimed to explore the influencing factors of red light running behavior and identify the differences between the DEB riders and the ordinary e-bike (OEB) riders to aid the development of countermeasures. In this study, the mixed (random parameter) binary logistic model was employed to capture the effects of unobserved heterogeneity. With this approach, factors including individual characteristics, behavioral variables, characteristics of signalized intersections, and the traffic environment were examined. Additionally, to account for the combined influence on the RLR occurrence, mixed logit framework was developed to reveal the correlations among the random parameters. The data of e-bike riders’ crossing behaviors at four signalized intersections in Xi'an, China were collected, and 3335 samples were recorded. The results indicated showed that DEB riders are more likely to run red lights than OEB riders. Factors that affect RLR behaviors of the two groups are different. Factors associated with the unobserved heterogeneity include red-light stage, observation time, age and waiting position of the rider. The joint influence among random parameters further illustrates the complexity of the contributing factors of riders’ crossing behavior. Results from the models provide insights into the development of intervention systems to improve the traffic safety of e-bike riders at intersections.

ACS Style

Fan Zhang; Yanjie Ji; Huitao Lv; Xinwei Ma. Analysis of factors influencing delivery e-bikes’ red-light running behavior: A correlated mixed binary logit approach. Accident Analysis & Prevention 2021, 152, 105977 .

AMA Style

Fan Zhang, Yanjie Ji, Huitao Lv, Xinwei Ma. Analysis of factors influencing delivery e-bikes’ red-light running behavior: A correlated mixed binary logit approach. Accident Analysis & Prevention. 2021; 152 ():105977.

Chicago/Turabian Style

Fan Zhang; Yanjie Ji; Huitao Lv; Xinwei Ma. 2021. "Analysis of factors influencing delivery e-bikes’ red-light running behavior: A correlated mixed binary logit approach." Accident Analysis & Prevention 152, no. : 105977.

Journal article
Published: 25 September 2020 in IEEE Access
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It is different from the previous supervised learning algorithm based on personal travel questionnaire, the aim of this study is to develop an unsupervised learning methodology to estimate the docked bike-sharing users’ trip purposes using IC card data, which trip purposes were unknown from the dataset. The present study is able to extract the trip-chains, which is used to understand the complete individual trip process. A rigorous method is then proposed to interpret the purpose of each leg of the trip-chain using a continuous hidden Markov model (CHMM). This method effectively combines the Gaussian mixture model and the hidden Markov model, and realizes the inference based on trip-chains. It is intended to enhance the understanding of docked bike-sharing users’ transfer intention, which is different from most trip motivation recognition methods. The Gaussian mixture layer uses the feature space constructed by the spatial and temporal information on trip-chains from the IC card data, as well as the land-use characteristics of the docked bike-sharing docking stations to complete the transfer of the trip-chains to the trip modes. The hidden Markov structure can realize the process from the trip modes to the trip purposes. The IC card data of docked bike-sharing usage in Nanjing, China is used to interpret the specific steps of the proposed model. A questionnaire survey is conducted to obtain the real trip purposes, which is compared with the estimated results from the model to verify the effectiveness of the model.. The results show that the accuracies of single trip recognition and chain trip recognition are 0.770 and 0.756, respectively. Compared with the baseline algorithm, the model also shows good performance. Therefore, the proposed approach can be used to discover and interpret the trip purpose using the IC card data.

ACS Style

Wenhao Li; Yanjie Ji; Xianqi Cao; Xinyi Qi. Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model. IEEE Access 2020, 8, 189598 -189613.

AMA Style

Wenhao Li, Yanjie Ji, Xianqi Cao, Xinyi Qi. Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model. IEEE Access. 2020; 8 (99):189598-189613.

Chicago/Turabian Style

Wenhao Li; Yanjie Ji; Xianqi Cao; Xinyi Qi. 2020. "Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model." IEEE Access 8, no. 99: 189598-189613.

Journal article
Published: 18 July 2020 in Transportation Research Part A: Policy and Practice
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The co-existence of traditional docked bike-sharing and emerging dockless systems presents new opportunities for sustainable transportation in cities all over the world, both serving door to door trips and accessing/egressing to/from public transport systems. However, most of the previous studies have separately examined the travel patterns of docked and dockless bike-sharing schemes, whereas the difference in travel patterns and the determinants of user demand for both systems have not been fully understood. To fill this gap, this study firstly compares the travel characteristics, including travel distance, travel time, usage frequency and spatio-temporal travel patterns by exploring the smart card data from a docked bike-sharing scheme and trip origin–destination (OD) data from a dockless bike-sharing scheme in the city of Nanjing, China over the same spatio-temporal dimension. Next, this study examines the influence of the bike-sharing fleets, socio-demographic factors and land use factors on user demand of both bike-sharing systems using multi-sourced data (e.g., trip OD information, smart card, survey, land use information, and housing prices data). To this end, geographically and temporally weighted regression (GTWR) models are built to examine the determinants of user demand over space and time. Comparative analysis shows that dockless bike-sharing systems have a shorter average travel distance and travel time, but a higher use frequency and hourly usage volume compared to docked bike-sharing systems. Trips of docked and dockless bike-sharing on workdays are more frequent than those on weekends, especially during the morning and evening rush hours. Significant differences in the spatial distribution between docked and dockless bike-sharing systems are observed in different city areas. The results of the GTWR models reveal that hourly docked bike-sharing trips and dockless bike-share trips influence each other throughout the week. The density of Entertainment points of interest (POIs) is positively correlated with the usage of dockless bike-sharing, but negatively correlated with docked bike-sharing usage. On the contrary, the proportion of the elderly has a positive association with the usage of docked bike-sharing, but a negative association with the usage of dockless bike-sharing. Finally, policy implications and suggestions are proposed to improve the performance of docked and dockless bike-sharing systems, such as increasing the flexibility of docked bike-sharing, designing and promoting mobile applications (APP) for docked bike-sharing, improving the quality of dockless shared bikes, and implementing dynamic time-based pricing strategies for dockless bike-sharing.

ACS Style

Xinwei Ma; Yanjie Ji; Yufei Yuan; Niels Van Oort; Yuchuan Jin; Serge Hoogendoorn. A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data. Transportation Research Part A: Policy and Practice 2020, 139, 148 -173.

AMA Style

Xinwei Ma, Yanjie Ji, Yufei Yuan, Niels Van Oort, Yuchuan Jin, Serge Hoogendoorn. A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data. Transportation Research Part A: Policy and Practice. 2020; 139 ():148-173.

Chicago/Turabian Style

Xinwei Ma; Yanjie Ji; Yufei Yuan; Niels Van Oort; Yuchuan Jin; Serge Hoogendoorn. 2020. "A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data." Transportation Research Part A: Policy and Practice 139, no. : 148-173.

Journal article
Published: 03 July 2020 in Travel Behaviour and Society
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This paper examines the determinants of young commuters’ frequency of using public bikes as a feeder mode to/from metro. Using three-week metro- and public bike- smart card data from Nanjing, 1,154 metro-bikeshare commuters aged 18–35 were extracted. As possible factors influencing the use of the combined mode, individual and household socio-demographics, travel-related attributes and built environment characteristics were extracted from multi-source data. A negative binomial regression model was used to examine the effects of these factors on usage frequency. We found that young commuters are the biggest group using metro-bikeshare system. They use public bikes frequently to transfer to/from metro when the cycling time is less than 10 min and the transfer happens during the morning peak. Built environment characteristics also influence usage frequencies, with high-density bike facilities being related to higher cycling rates in inner areas, and residential /employment locations related to lower rates of cycling in the core areas. This suggests that different measures and policies designed to encourage the integrated use of metro-bikeshare should be put forward for different regions.

ACS Style

Yang Liu; Yanjie Ji; Tao Feng; Harry Timmermans. Understanding the determinants of young commuters’ metro-bikeshare usage frequency using big data. Travel Behaviour and Society 2020, 21, 121 -130.

AMA Style

Yang Liu, Yanjie Ji, Tao Feng, Harry Timmermans. Understanding the determinants of young commuters’ metro-bikeshare usage frequency using big data. Travel Behaviour and Society. 2020; 21 ():121-130.

Chicago/Turabian Style

Yang Liu; Yanjie Ji; Tao Feng; Harry Timmermans. 2020. "Understanding the determinants of young commuters’ metro-bikeshare usage frequency using big data." Travel Behaviour and Society 21, no. : 121-130.

Journal article
Published: 23 March 2020 in IEEE Access
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Dockless bike-sharing has provided a flexible and convenient travel mode in recent years, with which shared bikes can be rented and returned easily. However, addressing the imbalance between demand and supply effectively becomes a major challenge to guarantee the sustainability of such systems. Vehicle-based approach results in substantial operating costs and more emissions. This study proposed a user-based model to incentivize users to participate in bike rebalancing. This model contains three important components: 1) determination of a bike surplus region by sorting surplus level in descend order; 2) determination of a bike deficient region from the perspective of users’ utility maximization; and 3) updating the states of regions. Several numerical tests were implemented to evaluate the model’s performance based on predefined parameters and historical trip data in downtown of Nanjing. As the results indicate, unmet demand can be greatly reduced if providing a certain level of monetary incentive. Different values of the maximum acceptable walking distance make little difference in demand loss and operating cost at a low level of incentive. With regard to the users, it implies that higher proportion of cooperative customers who are willing to participate in rebalancing can lead to a larger reduction of unmet demand. In addition, using surplus level as the index to define bike surplus regions is beneficial to saving the overall operating cost.

ACS Style

Yanjie Ji; Xue Jin; Xinwei Ma; Shuichao Zhang. How Does Dockless Bike-Sharing System Behave by Incentivizing Users to Participate in Rebalancing? IEEE Access 2020, 8, 58889 -58897.

AMA Style

Yanjie Ji, Xue Jin, Xinwei Ma, Shuichao Zhang. How Does Dockless Bike-Sharing System Behave by Incentivizing Users to Participate in Rebalancing? IEEE Access. 2020; 8 (99):58889-58897.

Chicago/Turabian Style

Yanjie Ji; Xue Jin; Xinwei Ma; Shuichao Zhang. 2020. "How Does Dockless Bike-Sharing System Behave by Incentivizing Users to Participate in Rebalancing?" IEEE Access 8, no. 99: 58889-58897.

Journal article
Published: 14 February 2020 in Sustainability
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Promoting a transition in individuals’ travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro–bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots; (2) the distribution of cycling times between origins/destinations and metro stations; and (3) the times when metro–bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro–bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers’ use frequency of integrated metro–bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems.

ACS Style

Yang Liu; Yanjie Ji; Tao Feng; Zhuangbin Shi. Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China. Sustainability 2020, 12, 1426 .

AMA Style

Yang Liu, Yanjie Ji, Tao Feng, Zhuangbin Shi. Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China. Sustainability. 2020; 12 (4):1426.

Chicago/Turabian Style

Yang Liu; Yanjie Ji; Tao Feng; Zhuangbin Shi. 2020. "Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China." Sustainability 12, no. 4: 1426.

Article
Published: 10 February 2020 in Transportation
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The recent development of shared-bike systems in China has brought convenience for users, as well as great pressure on bicycle parking management. There is limited empirical evidence regarding the effectiveness of incentive measures from the perspective of guiding people to park their shared bikes regularly. By defining a mixed logit model based on the theory of planned behaviour, this study focuses on exploring the conformity effects of policy compliance among dockless shared-bike users in the behavioural decisions of bicycle parking. A total of 453 respondents who had just finished parking a shared bike were invited to provide intentional information by participating in a stated preference survey. The empirical results indicated that, as a positive and negative incentive measure, both monetary rewards and financial penalties can motivate people to park the shared bikes in an unsaturated place near their destinations. However, the significant difference is that, with increasing incentive intensity, a monetary reward can motivate shared-bike users to shift the bicycles away more effectively than a financial penalty, especially when the shifting distance requires more than 10 min of walking. In addition, some factors used to characterize the individual heterogeneity, such as gender, education and searching time for dockless shared bikes, also have obvious impacts on policy compliance regarding bicycle parking guidance. These findings can help policy makers to create appropriate measures in the form of incentives to reduce illegal parking by shared-bike users.

ACS Style

Liangpeng Gao; Yanjie Ji; Xingchen Yan; Yao Fan; Weihong Guo. Incentive measures to avoid the illegal parking of dockless shared bikes: the relationships among incentive forms, intensity and policy compliance. Transportation 2020, 48, 1033 -1060.

AMA Style

Liangpeng Gao, Yanjie Ji, Xingchen Yan, Yao Fan, Weihong Guo. Incentive measures to avoid the illegal parking of dockless shared bikes: the relationships among incentive forms, intensity and policy compliance. Transportation. 2020; 48 (2):1033-1060.

Chicago/Turabian Style

Liangpeng Gao; Yanjie Ji; Xingchen Yan; Yao Fan; Weihong Guo. 2020. "Incentive measures to avoid the illegal parking of dockless shared bikes: the relationships among incentive forms, intensity and policy compliance." Transportation 48, no. 2: 1033-1060.

Journal article
Published: 11 January 2020 in Journal of Cleaner Production
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Bike-sharing systems have rapidly expanded around the world. Previous studies found that docked and dockless bike-sharing systems are different in terms of user demand and travel characteristics. However, their usage regularity and its determinants have not been fully understood. This research aims to fill this gap by exploring smart card data of a docked bike-sharing scheme and GPS trajectory data of a dockless bike-sharing scheme in Nanjing, China, over the same period. Both docked and dockless bike-sharing users can be classified into regular users and occasional users according to their usage frequency. Two systems are cross-compared regarding their travel characteristics. Then, binary logistic models are applied to reveal the impacts of travel characteristics and built environment factors on the regularity of bike-sharing usage. Results show that for both bike-sharing systems, regular users and occasional users share similar riding time and distance, while significant differences in the spatio-temporal distribution between docked and dockless bike-sharing systems are observed. The regression model results show that the “Trips during morning and afternoon peak hours” are positively associated with the regularity of both docked and dockless bike-sharing usage. However, the “Riding distance” variable is negatively associated with the usage regularity of both systems. Built environment factors including working point of interest (POI), residential POI, and transit POI promote the usage regularity of both bike-sharing systems. Finally, policy implications are proposed, such as increasing the density of docking stations in suburban areas and developing high-quality parking area for dockless bike-sharing around public transport stations. This study can help operators or governments to launch or improve the service of bike-sharing systems.

ACS Style

Yanjie Ji; Xinwei Ma; Mingjia He; Yuchuan Jin; Yufei Yuan. Comparison of usage regularity and its determinants between docked and dockless bike-sharing systems: A case study in Nanjing, China. Journal of Cleaner Production 2020, 255, 120110 .

AMA Style

Yanjie Ji, Xinwei Ma, Mingjia He, Yuchuan Jin, Yufei Yuan. Comparison of usage regularity and its determinants between docked and dockless bike-sharing systems: A case study in Nanjing, China. Journal of Cleaner Production. 2020; 255 ():120110.

Chicago/Turabian Style

Yanjie Ji; Xinwei Ma; Mingjia He; Yuchuan Jin; Yufei Yuan. 2020. "Comparison of usage regularity and its determinants between docked and dockless bike-sharing systems: A case study in Nanjing, China." Journal of Cleaner Production 255, no. : 120110.

Research article
Published: 17 July 2019 in IET Intelligent Transport Systems
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Smart card data (SCD) provide a new perspective for analysing the long-term spatiotemporal travel characteristics of public transit users. Understanding the commuting patterns provides useful insights for urban traffic management. This study attempts to identify and cluster commuting patterns and explore the influencing factors by combining SCD and traditional household travel survey data (HTSD) in Nanjing, China. First, the authors generate the commuting regularity rules using one-day HTSD. Then, the regular metro commuters are identified in four-week (20-weekday) SCD. Using the clustering method of the Gaussian mixture model, they classify metro commuters in SCD into three commuting pattern groups, namely, classic pattern, off-peak pattern, and long-distance pattern, based on their spatiotemporal characteristics. Next, they identify the corresponding metro commuters of these three groups in HTSD and apply a mixed logit regression model to determine the factors influencing metro commuting patterns from multiple dimensions. The results show that some socioeconomic attributes (e.g. gender, age, annual income, education, and occupation) as well as bus station density, metro lines, transfer mode, and transfer distance significantly impact commuting patterns. The findings can provide valuable information for planners and managers to put forward relevant transport guiding measures for alleviating traffic congestion and improving urban traffic management.

ACS Style

Yanjie Ji; Yu Cao; Yang Liu; Weihong Guo; Liangpeng Gao. Research on classification and influencing factors of metro commuting patterns by combining smart card data and household travel survey data. IET Intelligent Transport Systems 2019, 13, 1525 -1532.

AMA Style

Yanjie Ji, Yu Cao, Yang Liu, Weihong Guo, Liangpeng Gao. Research on classification and influencing factors of metro commuting patterns by combining smart card data and household travel survey data. IET Intelligent Transport Systems. 2019; 13 (10):1525-1532.

Chicago/Turabian Style

Yanjie Ji; Yu Cao; Yang Liu; Weihong Guo; Liangpeng Gao. 2019. "Research on classification and influencing factors of metro commuting patterns by combining smart card data and household travel survey data." IET Intelligent Transport Systems 13, no. 10: 1525-1532.

Journal article
Published: 04 July 2019 in Sustainability
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Since the long dwell time and chaotic crowds make metro trips inefficient and dissatisfying, the importance of optimizing alighting and boarding processes has become more prominent. This paper focuses on the adjustment of passenger organizing modes. Using field data from the metro station in Nanjing, China, a micro-simulation model of alighting and boarding processes based on an improved social force paradigm was built to simulate the movement of passengers under different passenger organizing modes. Unit flow rate, delay, and social force work (SFW) jointly reflect the efficiency and, especially, the physical energy consumption of passengers under each mode. It was found that when passengers alighted and boarded by different doors, efficiency reached its optimal level which was 76.92% higher than the status quo of Nanjing, and the physical energy consumption was reduced by 16.30%. Both the findings and the model can provide support for passenger organizing in metro stations, and the concept of SFW can be applied to other scenes simulated by the social force model, such as evacuations of large-scale activities, to evaluate the physical energy consumption of people.

ACS Style

Jiajie Yu; Yanjie Ji; Liangpeng Gao; Qi Gao. Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China. Sustainability 2019, 11, 3682 .

AMA Style

Jiajie Yu, Yanjie Ji, Liangpeng Gao, Qi Gao. Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China. Sustainability. 2019; 11 (13):3682.

Chicago/Turabian Style

Jiajie Yu; Yanjie Ji; Liangpeng Gao; Qi Gao. 2019. "Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China." Sustainability 11, no. 13: 3682.

Journal article
Published: 09 December 2018 in Sustainability
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Long-distance school commuting is a key aspect of students’ choice of car travel. For cities lacking school buses, the metro and car are the main travel modes used by students who have a long travel distance between home and school. Therefore, encouraging students to commute using the metro can effectively reduce household car use caused by long-distance commuting to school. This paper explores metro ridership at the station level for trips to school and return trips to home in Nanjing, China by using smart card data. In particular, a global Poisson regression model and geographically weighted Poisson regression (GWPR) models were used to examine the effects of the built environment on students’ metro ridership. The results indicate that the GWPR models provide superior performance for both trips to school and return trips to home. Spatial variations exist in the relationship between the built environment and students’ metro ridership across metro stations. Built environments around metro stations, including commercial-oriented land use; the density of roads, parking lots, and bus stations; the number of docks at bikeshare stations; and the shortest distance between bike stations and metro stations have different impacts on students’ metro ridership. The results have important implications for proposing relevant policies to guide students who are being driven to school to travel by metro instead.

ACS Style

Yang Liu; Yanjie Ji; Zhuangbin Shi; Liangpeng Gao. The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models. Sustainability 2018, 10, 4684 .

AMA Style

Yang Liu, Yanjie Ji, Zhuangbin Shi, Liangpeng Gao. The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models. Sustainability. 2018; 10 (12):4684.

Chicago/Turabian Style

Yang Liu; Yanjie Ji; Zhuangbin Shi; Liangpeng Gao. 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models." Sustainability 10, no. 12: 4684.

Articles
Published: 04 November 2018 in Transportation Planning and Technology
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Transfer points between metro and bus services remain an elusive, yet critical junction for transportation practitioners. Based on massive Smart Card (SC) data, previous studies apply a one-size-fits-all criterion to discriminate between transfers. However, this is not sufficiently convincing for different transfer pairs. To counter this problem, this study applies an association rules algorithm and cluster analysis to recognize metro-to-bus transfers using SC data, and demonstrates transfer recognition in a case study based on SC data collected during a week in Nanjing, China. It is shown that 85% of the transfer-recognition results are quite stable through the whole week, and the median transfer time between metro and bus is below 20 min. The method proposed in this study can be used to identify the busiest transfer points and to obtain average transfer times, which facilitates a smarter and more efficient public transit network.

ACS Style

De Zhao; Wei Wang; Chenyang Li; Yanjie Ji; Xiaojian Hu; Wenfu Wang. Recognizing metro-bus transfers from smart card data. Transportation Planning and Technology 2018, 42, 70 -83.

AMA Style

De Zhao, Wei Wang, Chenyang Li, Yanjie Ji, Xiaojian Hu, Wenfu Wang. Recognizing metro-bus transfers from smart card data. Transportation Planning and Technology. 2018; 42 (1):70-83.

Chicago/Turabian Style

De Zhao; Wei Wang; Chenyang Li; Yanjie Ji; Xiaojian Hu; Wenfu Wang. 2018. "Recognizing metro-bus transfers from smart card data." Transportation Planning and Technology 42, no. 1: 70-83.

Journal article
Published: 30 October 2018 in Sustainability
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Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.

ACS Style

Xinwei Ma; Yanjie Ji; Yuchuan Jin; Jianbiao Wang; Mingjia He. Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model. Sustainability 2018, 10, 3949 .

AMA Style

Xinwei Ma, Yanjie Ji, Yuchuan Jin, Jianbiao Wang, Mingjia He. Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model. Sustainability. 2018; 10 (11):3949.

Chicago/Turabian Style

Xinwei Ma; Yanjie Ji; Yuchuan Jin; Jianbiao Wang; Mingjia He. 2018. "Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model." Sustainability 10, no. 11: 3949.

Journal article
Published: 06 August 2018 in Transport Policy
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Though metro systems are established in many Chinese cities including Nanjing, they have yet covered every corner of a city. Bikeshare as a feeder mode to metro helps solve the last mile problem. Thus, it is necessary to monitor and analyze metro-bikeshare transfer characteristics. The primary objective of this study is to derive a reproducible methodology that isolates bicycle-metro transfer trips using smart card data. Two recognition rules proposed are a maximum transfer time of 10 min and a maximum transfer distance of 300 m. To explore the general characteristics of metro-bikeshare transfer trips, transfer stations served at less than 30 transfer trips during three consecutive weeks were eliminated to ensure that a non-typical transfer pattern would not distort the results. The results show that more than 89% passengers recognized have less than 6 transfers in 3 weeks, indicating that most users integrate bikeshare with metro impromptu. Two transfer peaks on workdays are during 7:00–9:00 and 17:00–19:00, especially in suburban areas, while at weekends, transfers show quite even during 8:00–19:00. As to “Return-Enter” and “Exit-Lease” transfer modes, the “time difference” phenomenon does exist, which means that the transfer peak of “Return-Enter”mode always happens one hour earlier than that of “Exit-Lease”. Furthermore, the demographic differences in metro-bikeshare usage pattern are revealed. Finally, policy implications are involved to improve the performance of metro-bikeshare integration for all kinds of people without creating inequality.

ACS Style

Xinwei Ma; Yanjie Ji; Mingyuan Yang; Yuchuan Jin; Xu Tan. Understanding bikeshare mode as a feeder to metro by isolating metro-bikeshare transfers from smart card data. Transport Policy 2018, 71, 57 -69.

AMA Style

Xinwei Ma, Yanjie Ji, Mingyuan Yang, Yuchuan Jin, Xu Tan. Understanding bikeshare mode as a feeder to metro by isolating metro-bikeshare transfers from smart card data. Transport Policy. 2018; 71 ():57-69.

Chicago/Turabian Style

Xinwei Ma; Yanjie Ji; Mingyuan Yang; Yuchuan Jin; Xu Tan. 2018. "Understanding bikeshare mode as a feeder to metro by isolating metro-bikeshare transfers from smart card data." Transport Policy 71, no. : 57-69.

Transportation engineering
Published: 21 July 2018 in KSCE Journal of Civil Engineering
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The household is usually an essential element for activity-based travel decision-making of individuals. From the perspective of household context, activities are often allocated to individuals based on their household roles, thereby affecting individuals’ travel behavior. By defining the household role using spatial-temporal constraints which are associated with individual activities and household activities, this paper investigates the travel mode choice of individuals considering the effect of different household roles. The descriptive statistics of the household roles and their corresponding travel mode choice are presented using the data from Kunming, China. The statistical results show that the modal splits between females and males perform a significant difference in the same household roles. Furthermore, the travel mode choice of females and males are estimated separately using multinomial logistic regression model. The results show that those who face more space-time constraints associated with household tasks are less willing to travel by car. While with the increase of commuting constraints, household heads, especially female-heads, tend to use car to meet the travel demands of household activity. Besides, individuals’ age, education level, the number of cars and bikes in household also have a significant impact on travel mode choices of individuals.

ACS Style

Yanjie Ji; Yang Liu; Qiyang Liu; Baohong He; Yu Cao. How Household Roles Influence Individuals’ Travel Mode Choice under Intra-household Interactions? KSCE Journal of Civil Engineering 2018, 22, 4635 -4644.

AMA Style

Yanjie Ji, Yang Liu, Qiyang Liu, Baohong He, Yu Cao. How Household Roles Influence Individuals’ Travel Mode Choice under Intra-household Interactions? KSCE Journal of Civil Engineering. 2018; 22 (11):4635-4644.

Chicago/Turabian Style

Yanjie Ji; Yang Liu; Qiyang Liu; Baohong He; Yu Cao. 2018. "How Household Roles Influence Individuals’ Travel Mode Choice under Intra-household Interactions?" KSCE Journal of Civil Engineering 22, no. 11: 4635-4644.

Journal article
Published: 14 June 2018 in Transport Policy
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Since many parents travel separately for escorting and commuting, certain hidden daily car trips may have been ignored in previous research regarding parental escort behaviors. By defining an escort-space model using the spatial relationships between home, the workplace, and school, this study focuses on the daily modal split among parental chauffeurs using data from Qujing, China, while focusing on the effects of different escort-space models: spatial aggregation, job-housing separation and school-housing separation. The descriptive statistics of parental chauffeurs’ travel mode choices under the influences of these three escort-space models are presented. The statistical results demonstrate that the modal splits of parental chauffeurs perform significantly differently under these three escort-space models. Furthermore, the determinants of the daily travel mode of parental chauffeurs, including escort-spaces and other selected variables, are investigated using a multinomial logit model. A model without the escort-space model is also presented for comparison. The results show that the model with the escort-space model has a more significant goodness-of-fit than the model without the escort-space model. Both the job-housing separation and school-housing separation of parental chauffeurs result in the increase of car trips, while the usage amount of car in daily journeys is higher than that in escort trips. Moreover, car ownership, bike ownership, household income, residential location, age, gender, income, and education level all significantly impact the daily travel mode choices of parental chauffeurs. These findings can help policymakers create suitable policies to reduce excessive car trips by parental chauffeurs.

ACS Style

Yang Liu; Yanjie Ji; Zhuangbin Shi; Baohong He; Qiyang Liu. Investigating the effect of the spatial relationship between home, workplace and school on parental chauffeurs’ daily travel mode choice. Transport Policy 2018, 69, 78 -87.

AMA Style

Yang Liu, Yanjie Ji, Zhuangbin Shi, Baohong He, Qiyang Liu. Investigating the effect of the spatial relationship between home, workplace and school on parental chauffeurs’ daily travel mode choice. Transport Policy. 2018; 69 ():78-87.

Chicago/Turabian Style

Yang Liu; Yanjie Ji; Zhuangbin Shi; Baohong He; Qiyang Liu. 2018. "Investigating the effect of the spatial relationship between home, workplace and school on parental chauffeurs’ daily travel mode choice." Transport Policy 69, no. : 78-87.

Research article
Published: 28 May 2018 in Journal of Advanced Transportation
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Smart card data provide valuable insights and massive samples for enhancing the understanding of transfer behavior between metro and public bicycle. However, smart cards for metro and public bicycle are often issued and managed by independent companies and this results in the same commuter having different identity tags in the metro and public bicycle smart card systems. The primary objective of this study is to develop a data fusion methodology for matching metro and public bicycle smart cards for the same commuter using historical smart card data. A novel method with association rules to match the data derived from the two systems is proposed and validation was performed. The results showed that our proposed method successfully matched 573 pairs of smart cards with an accuracy of 100%. We also validated the association rules method through visualization of individual metro and public bicycle trips. Based on the matched cards, interesting findings of metro-bicycle transfer have been derived, including the spatial pattern of the public bicycle as first/last mile solution as well as the duration of a metro trip chain.

ACS Style

De Zhao; Wei Wang; Ghim Ping Ong; Yanjie Ji. An Association Rule Based Method to Integrate Metro-Public Bicycle Smart Card Data for Trip Chain Analysis. Journal of Advanced Transportation 2018, 2018, 1 -11.

AMA Style

De Zhao, Wei Wang, Ghim Ping Ong, Yanjie Ji. An Association Rule Based Method to Integrate Metro-Public Bicycle Smart Card Data for Trip Chain Analysis. Journal of Advanced Transportation. 2018; 2018 ():1-11.

Chicago/Turabian Style

De Zhao; Wei Wang; Ghim Ping Ong; Yanjie Ji. 2018. "An Association Rule Based Method to Integrate Metro-Public Bicycle Smart Card Data for Trip Chain Analysis." Journal of Advanced Transportation 2018, no. : 1-11.