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

A Simple Method for Estimation of Queue Length

Urban arterials are characterized by frequent intersections. Queue length and delay are the two primary measures of performance of intersection. These measures play a primary role in determining the arterial performance. This article presents a methodology to determine the number of vehicles in queue at a signalized intersection for under saturated traffic conditions. The results obtained were validated using actual values that are manually extracted. The root-mean-square error is of the range 1.3 vehicles for estimation of number of vehicles in queue. The various aspects that have to be considered in accurate estimation of performance measures are also discussed

A Microsimulation-Based Stochastic Optimization Approach for Optimal Traffic Signal Design

Arriving at optimal signal timing parameters to improve the efficiency of traffic flow has been one of the major challenges faced by traffic engineers. The choice of a robust optimization framework and an accurate traffic model plays a significant role in determining optimal signal timing parameters. Though traffic flow is intuitively stochastic, few studies incorporate stochasticity in their optimization framework for traffic signal design. This study proposes two simulation-based stochastic optimization algorithms—an evolutionary algorithm-based framework and a simultaneous perturbation stochastic approximation (SPSA) algorithm-based framework for the optimal signal design of an isolated intersection using a calibrated microsimulation environment with reasonable accuracy. A software-in-the loop approach is used to control the traffic signals in the microsimulation environment. SPSA is a gradient descent algorithm with a powerful approach for approximating the gradient with just two function evaluations per gradient approximation. To evaluate the performance of the two frameworks, the study optimizes the signal timings for a case study on an isolated intersection in an urban arterial in Chennai. On comparing the two algorithms, it is found that SPSA performed better and took 100 function evaluations less than that taken by GA. A better (near optimal) initial solution is found to yield a faster rate of convergence for both algorithms. As the proposed optimization framework incorporates the stochastic nature of traffic in the optimization algorithm, it can accommodate the temporal variations in traffic and thereby provide traffic engineers a robust signal control strategy for improving the efficiency of traffic flow

Analysis of global positioning system based bus travel time data and its use for advanced public transportation system applications

The rapid advancements in sensor technologies has resulted in the increased use of Automatic Vehicle Location (AVL) systems for traffic data collection. Global Position System (GPS) sensors are the most commonly used AVL system, majorly because of it being a time-tested technology and being relatively cheap. Also, many of the transportation agencies have their vehicles equipped with GPS sensors. One of the interesting challenges in the field of Intelligent Transportation Systems (ITS) is to effectively mine useful information from such large-scale database accumulated over time. The current study analyses travel time data obtained from buses fitted with GPS devices in Chennai, India to understand its variation over time and space to find the spatial and temporal points of criticality. For this, Cumulative Frequency Distribution (CFD) curves, bar charts and boxplots were used. Inter-Quartile Range (IQR) was used as a measure to quantify the variations in travel time. Analysis showed that both travel time and its variation increased approximately 10% and 40%, respectively, from 2014 to 2016. This increase was observed to be primarily concentrated in six critical intersections during morning and evening peak hours. The findings from the study were further used in demonstrating possible user applications that can improve the efficiency of public transportation systems. As part of this, a real-time bus travel time prediction method was developed using a deep learning approach, Long and Short-Term Memory (LSTM) networks. Along with this, a robust fleet management system was also developed to check the adequacy of buses along the study corridor for different time of the day

Platoon Dispersion Analysis Based on Diffusion Theory

Urbanization and gro wing demand for travel, causes the traffic system to work ineffectively in most urban areas leadin g to traffic congestion. Many approaches have been adopted to address this problem, one among them being the signal co-ordination. This can be achieved if the platoon of vehicles that gets discharged at one signal gets green at consecutive signals with minimal delay. However, platoons tend to get dispersed as they travel and this dispersion phenomenon should be taken into account for effective signal coordination. Reported studies in this area are from the homogeneous and lane disciplined traffic conditions. This paper analyse the platoon dispersion characteristics under heterogeneous and lane-less traffic conditions. Out of the various modeling techniques reported, the approach based on diffusion theory is used in this study. The diffusion theory based models so far assumed thedata to follow normal distribution. However, in the present study, the data was found to follow lognormal distribution and hence the implementation was carried out using lognormal distribution. The parameters of lognormal distribution were calibrated for the study condition. For comparison purpose, normal distribution was also calibrated and the results were evaluated. It was foun d that model with log normal distribution performed better in all cases than the o ne with normal distribution

Platoon Dispersion Analysis Based on Diffusion Theory

Urbanization and gro wing demand for travel, causes the traffic system to work ineffectively in most urban areas leadin g to traffic congestion. Many approaches have been adopted to address this problem, one among them being the signal co-ordination. This can be achieved if the platoon of vehicles that gets discharged at one signal gets green at consecutive signals with minimal delay. However, platoons tend to get dispersed as they travel and this dispersion phenomenon should be taken into account for effective signal coordination. Reported studies in this area are from the homogeneous and lane disciplined traffic conditions. This paper analyse the platoon dispersion characteristics under heterogeneous and lane-less traffic conditions. Out of the various modeling techniques reported, the approach based on diffusion theory is used in this study. The diffusion theory based models so far assumed thedata to follow normal distribution. However, in the present study, the data was found to follow lognormal distribution and hence the implementation was carried out using lognormal distribution. The parameters of lognormal distribution were calibrated for the study condition. For comparison purpose, normal distribution was also calibrated and the results were evaluated. It was foun d that model with log normal distribution performed better in all cases than the o ne with normal distribution