Travel Behavior Analysis Using 2016 Qingdao's Household Traffic Surveys and Baidu Electric Map API Data

Gao, Ge; Wang, Zhen; Liu, Xinmin; Li, Qing; Wang, Wei; Zhang, Junyou
March 2019
Journal of Advanced Transportation;3/11/2019, p1
Academic Journal
Household traffic surveys are widely used in travel behavior analysis, especially in travel time and distance analysis. Unfortunately, any one kind of household traffic surveys has its own problems. Even all household traffic survey data is accurate, it is difficult to get the trip routes information. To our delight, electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, and Auto Navi Maps) could provide the trip route and time information, which remedies the traditional traffic survey's defect. Thus, we can take advantage of the two kinds of data and integrate them into travel behavior analysis. In order to test the validity of the Baidu electric map API data, a field study on 300 taxi OD pairs is carried out. According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data. Based on the fused data of household traffic survey and electric map API, travel behavior on trip time and distance is analyzed. Results show that most purposes' trip distances distributions are concentrated, which are no more than 10 kilometers. It is worth noting that students have the shortest travel distance and company business's travel distance distribution is dispersed, which has the longest travel distance. Compared to travel distance, the standard deviations of all purposes' travel time are greater than the travel distance. Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km.


Related Articles

  • Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting. Dongjoo Park; Laurence R.Rilett; Byron J.Gajewski; Clifford H.Spiegelman; Changho Choi // Transportation;Jan2009, Vol. 36 Issue 1, p77 

    With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions...

  • Convergence of a Discretised Travel-Time Model. Carey, Malachy; Ge, Y. E. // Transportation Science;Feb2005, Vol. 39 Issue 1, p25 

    In network models for dynamic traffic assignment (DTA), the travel time on a link is often treated as a function of the number of vehicles on the link. Instead of applying this model to the whole link, we divide the link into segments, apply the model (suitably adjusted)sequentially to these...

  • 'Going Up' is Made More Efficient. Garris, Leah B. // Buildings;May2010, Vol. 104 Issue 5, p32 

    The article discusses the sustainability benefits of using a destination dispatch elevator system. The system is said to be more efficient in terms of how building occupants are transported, aside from providing significant increases in passenger load at one time, which, in turn offers reduction...

  • Impact of heavy vehicles on surrounding traffic characteristics. Moridpour, Sara; Mazloumi, Ehsan; Mesbah, Mahmoud // Journal of Advanced Transportation;Jun2015, Vol. 49 Issue 4, p535 

    This work examines the impact of heavy vehicle movements on measured traffic characteristics in detail. Although the number of heavy vehicles within the traffic stream is only a small percentage, their impact is prominent. Heavy vehicles impose physical and psychological effects on surrounding...

  • EFFECTS OF VEHICLE NUMBER FEEDBACK IN MULTI-ROUTE INTELLIGENT TRAFFIC SYSTEMS. DONG, CHUANFEI; MA, XU; WANG, BINGHONG // International Journal of Modern Physics C: Computational Physics;Aug2010, Vol. 21 Issue 8, p1081 

    We first study dynamics of traffic flow with real-time information and the influence of a new feedback strategy named Vehicle Number Feedback Strategy (VNFS) in a multi-route scenario in which dynamic information can be generated and displayed on the board (the board refers to a variable message...

  • DATA DAZE.  // Planner;Oct2013, p50 

    This section presents the average minimum travel time across seven key services, which include work, primary school, hospital, and food store, for England in 2012.

  • Data fusion algorithm improves travel time predictions. Lim, S.; Lee, C. // IET Intelligent Transport System;Dec2011, Vol. 5 Issue 4, p302 

    Travel time is considered more useful to users than other travel-related information such as speed. It is mainly estimated by point or interval detection systems. In this study, the authors investigate the deficiency of these systems in estimating travel times when they are used in isolation,...

  • Control Strategies for an Idealized Public Transportation System. Osuna, E. E.; Newell, G. F. // Transportation Science;Feb72, Vol. 6 Issue 1, p52 

    Vehicles load passengers at a single service point and, after traversing some route, return for another trip. The travel times of successive trips are independent identically distributed random variables with a known distribution function. After a vehicle returns to the service point, one has...

  • Time Dependent Vehicle Routing Problem with Fuzzy Traveling Times Under Different Traffic Conditions. Demirel, Tufan; Demirel, Nihan Cetin; Tasdelen, Belgin // Journal of Multiple-Valued Logic & Soft Computing;2008, Vol. 14 Issue 3-5, p387 

    The basic vehicle routing problem model usually needs to be extended in order to solve real-world vehicle routing problems. Time dependent vehicle routing problem is a vehicle routing problem in which travel costs along the network are dependent upon the time of day during which travel is to be...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics