Estimation and prediction of travel time from loop detector data for intelligent transportation systems applications

With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. ?? Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. ?? A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. ?? Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results

Estimation and prediction of travel time from loop detector data for intelligent transportation systems applications

With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. ?? Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. ?? A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. ?? Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results

Significance of incorporating heterogeneity in a non-continuum macroscopic model for density estimation

The heterogeneity of traffic and the lack of lane discipline on the roads in India and other developing countries add complexity to the analysis and modeling of traffic. It is generally believed that it is important to take heterogeneity into account in traffic modeling. The aim of the present study is to check the validity of this assumption by analyzing the effect of incorporating heterogeneity in a macroscopic level traffic flow analysis. The application considered is real-time congestion analysis on Indian roads. Traffic density is considered as the congestion indicator. The measurement of density is difficult since it is a spatial parameter. It is usually estimated from other traffic parameters that can be readily measured using available sensors. A model-based estimation scheme using Kalman filtering has been employed to estimate traffic density. A non-continuum macroscopic model was attempted based on the lumped parameter approach. All the traffic variables were quantified without considering traffic lanes in order to take into account the lack of lane discipline. The effect of heterogeneity has been studied by incorporating static values of Passenger Car Units (PCU), dynamic values of Two Wheeler Units (TWU) and considering different classes of vehicles explicitly in the modeling process. The proposed estimation schemes without and with heterogeneity have been compared. The results have been corroborated using data collected from a road stretch in Chennai, India. The study shows that the significance of incorporating heterogeneity into the modeling of mixed traffic at the macroscopic level was not very significant

Dynamic trip planner for public transport using genetic algorithm

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles real-time Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem’s computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The “walk mode” and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner’s capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper

Travel Time Observations Using Bluetooth MAC Address Matching: A Case Study on the Rajiv Gandhi Roadway: Chennai, India

Bluetooth MAC Address matching has become a useful approach for determining travel times on corridors in the United States. In September 2013, an international collaborative study was performed using this technology along a busy urban corridor in Chennai, India. Two Purdue University graduate students traveled to Chennai, India to interact and understand the dynamics of exchanging knowledge and implementing technologies in different environments. The students worked with students from IIT Madras to determine the feasibility of Bluetooth probe vehicle technology along a typical Indian corridor. The study determined that it is feasible to expand Bluetooth use in India. Using the technology, the impact of weather, holiday and peak hour related traffic events were determined and evaluated. Of particular note were the relative high penetration of Bluetooth devices, and the exceptionally strong impact of precipitation on the heterogeneous traffic stream in Chennai, India

Integration of exponential smoothing with state space formulation for bus travel time and arrival time prediction

In recent years, the problem of bus travel time prediction is becoming more important for applications such as informing passengers regarding the expected bus arrival time in order to make public transit more attractive to the urban commuters. One of the popular techniques reported for such prediction is the use of time series analysis. Most of the studies on the application of time series techniques for bus arrival time prediction used Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) models, which are presently not suited for real time implementation. This is mainly due to the necessity and dependence of ARIMA models on a time series modelling software to execute. Moreover, the ARIMA model building process is time consuming, making it difficult to use for real-time implementations. Alternatively, Exponential Smoothing (ES) methods can be used, as they are easy to understand and implement when compared to ARIMA models. The present study is an attempt in this direction, where the basic equation of ES is used, as the state equation with Kalman filtering to recursively update the travel time estimate as the new observation becomes available. The proposed algorithm of state space formulation of ES with Kalman filtering for bus travel time and arrival time prediction was field tested using 105 actual bus trips data along a particular bus route from Chennai, India. The results are promising and a comparison of the proposed algorithm with ES alone without state space formulation and Kalman filtering has also been performed. An information system based on a webpage for real-time display of bus arrival times has been designed and developed using the proposed algorithm. First published online: 12 Oct 201

Reliable corridor level travel time estimation using probe vehicle data

Travel time information assists road users in making informed travel decisions such as mode choice, route choice and/or time of travel. This study explores the use of GPS data from buses and Wi-Fi and Bluetooth data from a sample of vehicles, for accurate estimation of the travel time of all vehicles on the roadway. A 5.5 km road stretch in Chennai city was selected as study stretch and data were collected for a week’s period. The present study develops models using linear regression and artificial neural network (ANN) techniquesFto estimate stream travel time using bus travel time obtained from GPS. ANN performed better compared to the linear regression for all sizes of segments. Most of the Indian cities have an integrated network of buses traveling on most of the road segments with on-board tracking devices, making this a useful development for real-time travel time estimation