Particle Filter for Reliable Bus Travel Time Prediction Under Indian Traffic Conditions

In recent times, traffic congestion has been increasing rapidly and deteriorating the quality of traffic systems in urban areas of many developed and developing countries. This became a serious problem faced by society, as many people are using private vehicles while commuting from one place to the other. One of the reasons people are shifting towards private transportation is due to lack of reliability of the public transportation systems. Attracting more travelers towards public transportation using Intelligent Transportation Systems (ITS) technologies is one way to reduce the negative impacts. In this context, prediction of bus travel time and providing information about bus arrival time to passengers accurately is a potential solution, which will help in reducing the uncertainty and waiting time associated uncertainties with public transit systems. However, for this solution to be effective, the information provided to passengers should be highly reliable. The present study proposes a model based prediction method that uses particle filtering technique for accurate prediction of bus travel times for the development of a real time passenger information system under heterogeneous traffic conditions that exist in India. The results obtained from the implementation of the above method are validated using the measured travel time. The prediction accuracy is quantified using the Mean Absolute Percentage Error (MAPE) and the performance is compared with a base approach namely, the historic average method. The quantified error in terms of MAPE is 20% for the proposed method and 37% for the historic average method, indicating the superiority of the proposed method over historic average method. Thus, it can be concluded that particle filter is a viable tool in the prediction of bus travel times.

[1]  Hironori Suzuki.  Dynamic State Estimation in Vehicle Platoon System by Applying Particle Filter and Unscented Kalman Filter , 2013 .

[2]  Ashish Verma,et al.  Achieving sustainable transportation system for Indian cities - problems and issues , 2011 .

[3]  Suwardo,et al.  ARIMA MODELS FOR BUS TRAVEL TIME PREDICTION , 2010 .

[4]  Lelitha Vanajakshi,et al.  Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses , 2009 .

[5]  Lelitha Vanajakshi,et al.  Comparison of Model Based and Machine Learning Approaches for , 2014 .

[6]  Yu Bin,et al.  Bus Arrival Time Prediction Using Support Vector Machines , 2006 .

[7]  Lionel M. Ni,et al.  SEER: Metropolitan-Scale Traffic Perception Based on Lossy Sensory Data , 2009, IEEE INFOCOM 2009.

[8]  Bin Yu,et al.  An Adaptive Bus Arrival Time Prediction Model , 2009 .

[9]  Lelitha Vanajakshi,et al.  Pattern-Based Bus Travel Time Prediction under Heterogeneous Traffic Conditions , 2014 .

[10]  Ruimin Li,et al.  Incorporating uncertainty into short-term travel time predictions , 2011 .

[11]  Hesham Rakha,et al.  Real-time travel time prediction using particle filtering with a non-explicit state-transition model , 2014 .

[12]  A. Hegyi,et al.  Parallelized particle filtering for freeway traffic state tracking , 2007, 2007 European Control Conference (ECC).

[13]  Yin Han,et al.  Study on the Prediction Model of Bus Arrival Time , 2009, 2009 International Conference on Management and Service Science.

[14]  Bin Ran,et al.  Traffic Estimation Based on Particle Filtering with Stochastic State Reconstruction Using Mobile Network Data , 2006 .

[15]  Amer Shalaby,et al.  PREDICTION MODEL OF BUS ARRIVAL AND DEPARTURE TIMES USING AVL AND APC DATA , 2004 .

[16]  Liping Fu,et al.  Predicting Bus Arrival Time on the Basis of Global Positioning System Data , 2007 .

[17]  Monica Nicoli,et al.  Estimation of highway traffic from sparse sensors: Stochastic modeling and particle filtering , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[18]  Laurence R. Rilett,et al.  Prediction Model of Bus Arrival Time for Real-Time Applications , 2005 .

[19]  Lelitha Vanajakshi,et al.  Pattern identification based bus arrival time prediction , 2014 .

[20]  Andreas Hegyi,et al.  Freeway traffic estimation within particle filtering framework , 2007, Autom..

[21]  Nour-Eddin El Faouzi,et al.  Particle filter-based real-time estimation and prediction of traffic conditions , 2007 .

[22]  Dong Zhang,et al.  Historical Travel Time Based Bus-Arrival-Time Prediction Model , 2011 .

[23]  Steven I-Jy Chien,et al.  Dynamic Bus Arrival Time Prediction with Artificial Neural Networks , 2002 .

[24]  Donald J Dailey,et al.  A PRESCRIPTION FOR TRANSIT ARRIVAL/DEPARTURE PREDICTION USING AUTOMATIC VEHICLE LOCATION DATA , 2003 .

[25]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[26]  Shaowu Cheng,et al.  Bus arrival time prediction model based on APC data , 2010 .

[27]  Rajat Rajbhandari,et al.  Bus arrival time prediction using stochastic time series and Markov chains , 2005 .

[28]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[29]  Lelitha Vanajakshi,et al.  Development of a real-time bus arrival prediction system for Indian traffic conditions , 2010 .