The common phenomenon of uneven headway in bus service is explored based on the automatic vehicle location （AVL） data of Route 2 in Yichun City of Jiangxi province from 6：00 to 9：00 in the morning. The headway reg...The common phenomenon of uneven headway in bus service is explored based on the automatic vehicle location （AVL） data of Route 2 in Yichun City of Jiangxi province from 6：00 to 9：00 in the morning. The headway regularity of two stages 6： 00--7：00 and 7： 00--9：00 is comparatively analyzed, and it is found that both the traffic conditions and the passenger demand affect headway regularity. A bus arrival model, which assumes that the dwell time of a bus is linear in headway, is built to probe the effect of scheduled headway, and the model is simulated by Matlab. The simulation results reveal that the departure intervals and fluctuations affect headway regularity. Longer intervals and less fluctuation mean higher regularity of headway. And, the fluctuation has a more obvious influence on headway regularity than the interval. Controlling the fluctuations of scheduled headway can effectively raise the regularity of headway and improve the level of public transport service.展开更多
To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was...To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.展开更多
As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate pa...As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate passenger boarding time, a regression analysis framework is proposed to capture the difference and influential factors of boarding time for adult and elderly passengers based on smart card data from Changzhou. Boarding gap, the time difference between two consecutive smart card tapping records, is calculated to approximate passenger boarding time. Analysis of variance is applied to identify whether the difference in boarding time between adults and seniors is statistically significant. The multivariate regression modeling approach is implemented to analyze the influences of passenger types, marginal effects of each additional boarding passenger and bus floor types on the total boarding time at each stop. Results show that a constant difference exists in boarding time between adults and seniors even without considering the specific bus characteristics. The average passenger boarding time decreases when the number of passenger increases. The existence of two entrance steps delays the boarding process, especially for elderly passengers.展开更多
基金The National Basic Research Program of China(973 Program)(No.2012CB725402)the National Natural Science Foundation of China(No.50978057)Program of Scientific Innovation Research of College Graduate in Jiangsu Province(No.CXLX12_0108)
文摘The common phenomenon of uneven headway in bus service is explored based on the automatic vehicle location （AVL） data of Route 2 in Yichun City of Jiangxi province from 6：00 to 9：00 in the morning. The headway regularity of two stages 6： 00--7：00 and 7： 00--9：00 is comparatively analyzed, and it is found that both the traffic conditions and the passenger demand affect headway regularity. A bus arrival model, which assumes that the dwell time of a bus is linear in headway, is built to probe the effect of scheduled headway, and the model is simulated by Matlab. The simulation results reveal that the departure intervals and fluctuations affect headway regularity. Longer intervals and less fluctuation mean higher regularity of headway. And, the fluctuation has a more obvious influence on headway regularity than the interval. Controlling the fluctuations of scheduled headway can effectively raise the regularity of headway and improve the level of public transport service.
基金Project(2011AA010101) supported by the National High Technology Research and Development Program of China
文摘To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.
基金The National Natural Science Foundation of China(No.51338003,71801041)
文摘As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate passenger boarding time, a regression analysis framework is proposed to capture the difference and influential factors of boarding time for adult and elderly passengers based on smart card data from Changzhou. Boarding gap, the time difference between two consecutive smart card tapping records, is calculated to approximate passenger boarding time. Analysis of variance is applied to identify whether the difference in boarding time between adults and seniors is statistically significant. The multivariate regression modeling approach is implemented to analyze the influences of passenger types, marginal effects of each additional boarding passenger and bus floor types on the total boarding time at each stop. Results show that a constant difference exists in boarding time between adults and seniors even without considering the specific bus characteristics. The average passenger boarding time decreases when the number of passenger increases. The existence of two entrance steps delays the boarding process, especially for elderly passengers.
