Open Access Open Access  Restricted Access Subscription or Fee Access

An Innovative Method of Travel Time Data Collection Using Mobile GPS Application

Jaya Krishna Jammula, KVR Ravishankar

Abstract


Travel time information is the basic need for any intelligent transportation system (ITS) applications. It is the qualitative check for any road infrastructure developed or proposed. For calculating travel time, many studies have been done across the world using various data collection techniques. Out of the different data collection techniques, GPS instrument is used in many studies due to its cost and accuracy. Many studies across the world have been done on inclusion of mobile phone and it’s applications in travel time data collection techniques. This study intended to check alternatives for the handheld GPS instrument. Many free applications are available in smartphones which do similar job as that of a conventional instrument. Mobile application OSM tracker is selected as the data collection technique in this study. Data are collected and analyzed to compare it with Trimble handheld GPS instrument. Statistical tests were conducted between speed, distance and travel time obtained using both the methods. A simple linear regression model is developed using data from OSM tracker considering speed and distance as independent variables to estimate travel times. For validation, data from last run of the vehicle during data collection are used and also been validated using data from another town named Siddipet in Telangana, India. From this study, it is concluded that OSM tracker can be used in travel time

Full Text:

PDF

References


Li Y, McDonald M. Link travel time estimation using single GPS equipped probe vehicle. The 5th International Conference on Intelligent Transportation Systems. 3–6 September 2002, Singapore.

Takahashi S, Izumi T. Application of genetic algorithm to travel time measurement using vehicle data provided from ultrasonic vehicle detectors. Electron Commun Jpn: Trans Inst Electr Eng Jpn. 2009; 128-C(1): 111–117p.

Zheng F, Zuylen H. Urban link travel time estimation based on sparse probe vehicle data. Transp Res C. 2013; 31: 145–157p.

Kumar SV, Vanajakshi L. Urban arterial travel time estimation using buses as probes. Arab J Sci Eng. 2014; 39: 7555–7567p.

Amita J, Singh J, Kumar G. Prediction of bus travel time using artificial neural network. Int J Traff Transp Eng. 2015; 5(4): 410–424p.

Fan W, Gurmu Z. Dynamic travel time prediction models for buses using only GPS data. Int J Transp Sci Technol. 2015; 4(4): 353–366p.

Araghi BN, Krishnan R, Lahrmann H. Mode-specific travel time estimation using Bluetooth technology. J Intell Transp Syst. 2016; 20(3): 219–228p.

Moghaddam SS, Hellinga B. Quantifying measurement error in arterial travel times measured by bluetooth detectors. Transp Res Rec J Transp Res Board. 2013; 2395:, 111–122p.

Satyakumar M, Anil R, Sivakumar B. Travel time estimation and prediction using mobile phones: a cost effective method for developing countries. Civ Eng Dimen J Civ Sc Appl, 2014; 16(1): 33–39p.

Bar-Gera H. Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: a case study from Israel.Kazagli E, Koutsopoulos HN. Estimation of arterial travel time from automatic number plate recognition data. J Transp Res Board. 2013; 2391: 22–31p.

Cohen S, Christoforou Z. Travel time estimation between loop detectors and FCD: a compatibility study on the Lille network, France. Transp Res Proc. 2015; 10: 245–255p.

Safi H, Assemi B, Mesbah M, Ferreira L. An empirical comparison of four technology-mediated travel survey methods. J Traff Transp Eng. 2017; 4(1): 80–87p.

Lu Y, Chang G. Stochastic model for estimation of time-varying arterial travel time and its variability with only link detector data. Transp Res Rec J Transp Res Board. 2012; 2283: 44–56p.

Padmanaban RPS, Divakar K, Vanajakshi L, Subramanian SC. Development of a real-time bus arrival prediction system for Indian traffic conditions. IET Intell Transp Syst. 2010; 4(3): 189–200p.

Ruo Y, Wei C, Shan L. The research on the city bus traffic time sequence prediction mode based on empirical decomposition mode. Adv Transp Stud. 2014; 3(Special Issue): 51–60p.

Li SW, Li Y, Yang J, Cao CT, Yang JF, Zhou HD. A study on bus passenger travel origin and destination based on spatio-temporal data fusion analysis. Adv Transp Stud. 2017; 1(Special Issue): 31–38p.

Vanajakshi L, Subramanian SC, Sivanandan R. Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses. IET Intell Transp Syst. 2008; 3(1): 1–9p.

Alrukaibi F, Alsaleh R, Sayed T. Real-time travel time estimation in partial network coverage: a case study in Kuwait City. Adv Transp Stud. 2018; XLIV: 79–94p.

Hunter T, Herring R, Abbeel P, Bayen A. Path and travel time inference from GPS probe vehicle data. Proc Neural Infor Proc Syst Found. 2009.

Siuhi S, Mwakalonge J. Opportunities and challenges of smart mobile applications in transportation. J Traff




DOI: https://doi.org/10.37628/jtets.v5i1.443

Refbacks

  • There are currently no refbacks.