Beijing Transportation Information Center

Beijing, China

Beijing Transportation Information Center

Beijing, China
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Wang Z.-J.,Beijing Jiaotong University | Chen F.,Beijing Jiaotong University | Wang B.,Beijing Transportation Information Center | Huang J.-L.,Beijing Transportation Information Center
Transportation | Year: 2017

Fare change is an effective tool for public transit demand management. An automatic fare collection system not only allows the implementation of complex fare policies, but also provides abundant data for impact analysis of fare change. This study proposes an assessment approach for analyzing the influence when substituting a flat-fare policy with a distance-based fare policy, using smart card data. The method can be used to analyze the impact of fare change on demand, riding distances, as well as price elasticity of demand at different time and distance intervals. Taking the fare change of Beijing Metro implemented in 2014 as a case study, we analyze the change of network demand at various levels, riding distances, and demand elasticity of different distances on weekdays and weekends, using the method established and the smart card data a week before and after the fare change. The policy implication of the fare change was also addressed. The results suggest that the fare change had a significant impact on overall demand, but not so much on riding distances. The greatest sensitivity to fare change is shown by weekend passengers, followed by passengers in the evening weekday peak time, while the morning weekday peak time passengers show little sensitivity. A great variety of passengers’ responses to fare change exists at station level because stations serve different types of land usage or generate trips with distinct purposes at different times. Rising fares can greatly increase revenue, and can shift trips to cycling and walking to a certain extent, but not so much as to mitigate overcrowding at morning peak times. The results are compared with those of the ex ante evaluation that used a stated preference survey, and the comparison illustrates that the price elasticity of demand extracted from the stated preference survey significantly exaggerates passengers’ responses to fare increase. © 2017 Springer Science+Business Media New York

Li H.,Tongji University | Tu H.,Tongji University | Liu H.,Beijing Transportation Information Center | Shi H.,Tongji University
Functional Pavement Design - Proceedings of the 4th Chinese-European Workshop on Functional Pavement Design, CEW 2016 | Year: 2017

Traffic states are important traffic information for travelers. The existing traffic regulations have a detailed and unique criterion for distinguishing the traffic states. Mostly, the traffic states are defined based on the objectively estimated travel speeds. Yet, it is reported that travelers' perceptions on travel speeds and traffic states might be different from the objective ones. This paper employs the empirical perception data collected in the city of Beijing to investigate travelers' perceived travel speeds and traffic states in relation to the objective ones. The effects of travelers' social characteristics on their perceived speed differences are explored as well. 120 videos, each recording a 5-second traffic condition data, are prepared representing a variety of traffic conditions on different road sections. The objectively estimated travel speed of each video and the associated traffic state based on the traffic regulations were pre-processed. 94 effective participants were asked to judge the traffic states of all 120 videos, and then to report their perceived travel speeds and traffic states for each video. The differences between the perceived and objectively estimated travel speeds and traffic states are analyzed based on the collected data. The results show that users do have significant perceived differences in travel speeds and traffic states. High heterogeneities are found among participants in their perceptions, especially among different gender, age and risk-attitude groups. This study suggests that the existing criterion for distinguishing the traffic states should be adjusted by considering travelers' perceived differences in travel speeds. © 2016 Taylor & Francis Group, London.

Song Z.,Beihang University | Zhu T.,Beihang University | Wu D.,Beijing Transportation Information Center | Lius S.,Beihang University
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI | Year: 2015

Traffic speed is one of the most essential parameters representing traffic conditions in intelligent traffic system (ITS). In recent years, there have been several approaches estimating traffic speed based on cellular network signaling data. However, the accuracy of these approaches is unsatisfactory because they have a poor performance in filtering out noisy data and minimizing deviations of traffic speed values' trend in adjacent time intervals. In this paper, a new approach is proposed to solve the two problems above. The approach filters out noisy data according to educated judgment, and adopts a modified Kalman filter algorithm to minimize the deviations. The performance study on real data sets of Beijing shows that the accuracy of the proposed approach is higher when compared with existing two notable estimation approaches. Further the approach will contribute to developing intelligent navigation systems and pursuing artificial intelligence applications. © 2014 IEEE.

Yang P.,Beihang University | Zhu T.,Beijing Transportation Information Center | Wan X.,Beihang University | Wang X.,Beihang University
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI | Year: 2015

Call detail records (CDRs) containing mass position information allow us to reveal characteristics about the city dynamics and human behaviors, which are crucial for policy decisions such as urban planning and transportation engineering. Being able to identify the trajectory and significant places is of prime importance. In this paper, we aim to extract trajectory from anonymized call detail records and adopt two-step clustering to obtain significant places from multi-day data. We propose a new method for mining trajectory by identifying users' stop and move state based on location gradient, which can be applied to users with low communication frequency. We analyze the feature of real CDR data and propose novel methods for noise handling. Home Time and Work Time are extracted from statistics of users' mobility pattern to recognize their significant places including home and work of a single day. Utilizing the characteristic of cyclical mobility, we conduct a cluster analysis to identify users' significant places which are not limited to one home or one work based on multi-day data. We run four experiments to show the robustness and stability of our method. During both typical stop and move period, our method performs better than state-of-art method. © 2014 IEEE.

PubMed | Beijing University of Technology, Beijing Transportation Coordination Center and Beijing Transportation Information Center
Type: Journal Article | Journal: Sensors (Basel, Switzerland) | Year: 2016

The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods.

Xiong J.,Beijing Jiaotong University | Guan W.,Beijing Jiaotong University | Sun Y.,Beijing Jiaotong University | Sun Y.,Beijing Transportation Information Center
Beijing Jiaotong Daxue Xuebao/Journal of Beijing Jiaotong University | Year: 2013

A short-term forecasting method of passenger flow in the metro transfer channel based on Kalman filter is proposed in this paper. The state equation of the system of metro passenger flow is formulated first. Then the state transition matrix of the state equation is obtained based on historical data. After that, a grey relation analysis method is applied in solving the state transition matrix of the time series to be tested. By this, the problem of the short-term forecasting of metro passenger flow can be solved. At last, the metro passenger flow forecasting of Xidan station in Beijing is taken as an example, and the short-term forecasting of the passenger flow in the morning rush hour in one week is proceeded from the two respects of weekdays and weekends.

Wang J.-C.,Beijing Transportation Information Center
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | Year: 2015

Nowadays using taxi booking app with Smartphone is a new way to take a taxi and it becomes more and more popular in Beijing. A data process model is put forward and the orders of taxi booking app is analyzed to find out the timely and spatial characteristics, such as night-peak, a low on Saturday and high instantaneity, linear increasement with ring-roads, and non-uniformity of kernel density spatially. This paper also studies the evaluation method of taxi booking app based on its comparison with the basic indexes of taxi management, and evaluates application effect of two periods. Copyright ©2015 by Science Press.

Wan X.,Beijing Transportation Information Center | Du Y.,Beijing Transportation Information Center | Wang J.,Beijing Transportation Information Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Dynamic Transportation Information Service has penetrated into residents' travels. The current problems that transportation information services face are variable such as real-time traffic forecasting, traffic managing and traffic induction. The above problems are related to the quality of historical traffic condition data. Due to a limited of GPS data collecting, the collected GPS data which scarcely covers the whole road network leads to incomplete and error traffic condition data. In consequence, two serious problems of traffic condition data quality manifest in incompleteness and low accuracy. This paper extends RD-PCA method which preliminarily focuses on the accuracy of imputing to prevent the estimating results from being impacted by outliers and aims at guaranteeing the completeness of imputing. The method excludes error data taking data quality measurement criterions. By adopting a measure factor, this method detects outliers and standardizes them, then constructs a robust feature space and imputes the missing data. The experimental results show that the proposed method can guarantee a high completeness and high accuracy under the condition of different missing rates. © 2014 Springer International Publishing Switzerland.

Wan X.,Beijing Transportation Information Center | Wang J.,Beijing Transportation Information Center | Zhong Y.,Beijing Transportation Information Center | Du Y.,Beijing Transportation Information Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Travelling by taxi is more convenient and effective. With an overcrowding population and a much terrible traffic, the traditional way of hailing a taxi encounters many challenges like where to pick-up/drop-off passengers reasonably and where to find potential passengers quickly. More cities have established taxi stands to advocate and to guide passengers to hail a taxi. However, most of them have low rate of usage. The reason lies in that to determine where to establish reasonably is a big problem. In this paper, we are the first to propose a DFA to identify data signifying pick-up/drop-off events. We propose a DBH-CLUS method to identify pick-up/drop-off hotspots. The method applies hierarchal clustering based on agglomerative clustering analysis method. We have conducted three experiments to verify the DFA, to analyze the region agglomeration and to analyze the accuracy. The experimental results manifest that our method can precisely identify hotspots from the original GPS data and provide an excellent tool to facilitate taxi stand planning. © Springer International Publishing Switzerland 2015.

Li J.,Beijing Transportation Information Center
ICTIS 2013: Improving Multimodal Transportation Systems - Information, Safety, and Integration - Proceedings of the 2nd International Conference on Transportation Information and Safety | Year: 2013

In order to enhance the application value of the transportation data center further, it was essential to use business intelligence (BI) technology for data mining, analyzing, and displaying. On the basis of summarizing the status and trend of BI technology for data display and analysis, the general methods of the analysis process for the data center were summarized. Taking the taxi meter data of Beijing as an example, the structure of the original data was analyzed and the calculation method for the taxi operation index was put forward. The indexes were put forward, including operation mileage, operation time, number of carrying passengers, income, and so on. They were analyzed from different dimensions using BI technology. The functions of historical data query, report output, and others were provided, supporting the taxi operation management and decision. © 2013 American Society of Civil Engineers.

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