Estimating Link Travel Time with Sparse GPS Data on Highway Corridors

Author(s):  
Qi Gong ◽  
Teresa M. Adams ◽  
Xiubin Bruce Wang
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Jian Wang ◽  
Yang Cao

Bus travel time is an important source of data for time of day partition of the bus route. However, in practice, a bus driver may deliberately speed up or slow down on route so as to follow the predetermined timetable. The raw GPS data collected by the GPS device equipped on the bus, as a result, cannot reflect its real operating conditions. To address this concern, this study first develops a method to identify whether there is deliberate speed-up or slow-down movement of a bus. Building upon the relationships between the intersection delay, link travel time, and traffic flow, a recovery method is established for calculating the real bus travel time. Using the dwell time at each stop and the recovered travel time between each of them as the division indexes, a sequential clustering-based time of day partition method is proposed. The effectiveness of the developed method is demonstrated using the data of bus route 63 in Harbin, China. Results show that the partition method can help bus enterprises to design reasonable time of day intervals and significantly improve their level of service.


2014 ◽  
Vol 8 (1) ◽  
pp. 130-135
Author(s):  
S. Nithya ◽  
D. Senthurkumar ◽  
K. .Gunasekaran

The travel time studies are one of the most important measures used for evaluating the performance of road networks. The Global Positioning System (GPS) is a space-based system that provides position and time information in all weather conditions. GPS data could be used to obtain the values of traffic control delay, vehicle queue, average travel time and vehicle acceleration and deceleration at intersections.The task of estimation of delay becomes complex if it is performed for intersections carrying heterogeneous traffic and that to for over saturated conditions. Most of the urban signalized intersections are manually controlled during peak hours. GPS device fitted in a vehicle was run repeatedly during morning peak period and the period during which vehicles were allowed to cross the intersection was recorded with video graphic camera. The attempt to identify the control delay with the GPS data from the test vehicle while crossing manually operated major intersection is presented in this paper.


Author(s):  
Meiping Yun ◽  
Wenwen Qin

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.


2003 ◽  
Vol 36 (14) ◽  
pp. 137-141 ◽  
Author(s):  
Alexandre Torday ◽  
André-Gilles Dumont

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