Development of an optimal signal control strategy for Heterogeneous Less Lane-Disciplined traffic conditions

This research focuses on addressing the issue of inefficient operation of traffic signals under Heterogeneous and Less-Lane-Disciplined (HLLD) traffic conditions in developing countries by proposing an optimal signal design strategy. The main highlights of the research are a methodology to identify critical intersections, a methodology to select the optimal signal phasing, a novel queueing theory based theoretical delay model that accounts for HLLD traffic characteristics, a robust method to simultaneously estimate delay and density, and a real-time delay based dynamic control strategy. The proposed strategy reduces control delays by up to 33% compared to in-practice approaches under HLLD traffic conditions

Simultaneous Prediction of Midblock and Intersection Traffic States on Urban Arterials

Reliable, real-time prediction of delay and density is challenging as direct measurement of these variables is difficult. Though studies yielding reasonably accurate predictions of delay and density are reported in the literature, a comprehensive methodology to simultaneously predict both delay and density is lacking. Hence, a recursive technique that uses minimal real-time data for dynamic simultaneous prediction of midblock density and intersection delay is proposed. This study uses conservation equation-based recursive prediction of the number of vehicles inside the midblock section (density), which in turn is used to predict delay using shockwave theory. The Kalman Filter is a one-step-ahead density prediction method that can yield reliable density predictions even under the presence of errors in detector data. The one-step-ahead delay predictions obtained had a Mean Absolute Percentile Error (MAPE) of 10.4%, whereas the one-step-ahead density predictions obtained had a MAPE of 9.96%. Due to its robustness, this method can be used to arrive at one-step-ahead predictions of parameters like delay and queue length for any traffic scenario for which shockwave diagrams can be produced. </p

Traffic State Estimation near Signalized Intersections

The primary goal with which any transportation system is designed is to make efficient use of the available infrastructure to achieve better level of service (LoS). However, LoS is observed to be deteriorating on urban roads, especially near signalized intersections, primarily due to the suboptimal operation of traffic signals. To achieve optimal performance of traffic signals, knowledge about the traffic states prevalent in the vicinity of the intersection is essential. Traffic states in general can be estimated at both macroscopic and microscopic level by employing various mathematical and data-driven approaches. However, obtaining these variables near the intersection is difficult and challenging under varying traffic conditions. This paper presents a systematic review of the state of the art in traffic state estimation (TSE) near signalized intersections both under homogeneous, lane-based, and heterogeneous less lane disciplined (HLLD) traffic conditions. This is expected to be a guide to traffic engineers, decision makers, and researchers aiming to gain pertinent knowledge about the sensors that can be used, data that needs to be collected, estimation methods that are suitable, and the intersection performance measures that need to be evaluated. The gaps in the current state of the art and future research directions are highlighted. In addition, insights on ways to address challenges pertaining to TSE near intersections under HLLD traffic conditions are also discussed.</p

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

Development of a Theoretical Delay Model for Heterogeneous and Less Lane-Disciplined Traffic Conditions

In developing countries with limited or no availability of traffic sensors, theoretical delay models are the most commonly used tool to estimate control delay at intersections. The traffic conditions in such countries are characterised by a large mix of vehicle types and limited or no lane discipline (Heterogeneous, Less Lane-Disciplined (HLLD) traffic conditions), resulting in significantly different traffic dynamics. This research develops a queueing theory-based theoretical delay model that explicitly incorporates HLLD traffic conditions' characteristic features like lack of lane discipline, violation of the First-In-First-Out rule, and a large mix of vehicle types. A new saturation flow-based Passenger Car Equivalent (PCE) estimation methodology to address heterogeneity and a virtual lane estimation approach to address lack of lane-discipline are proposed. The developed model shows 64% lesser error in average control delay estimation compared to the in-practice delay estimation models under HLLD traffic conditions. The developed model is used for signal optimisation under HLLD traffic conditions and reductions of up to 24% in control delay in comparison to the in-practice signal timing approach are observed. The study also highlights the significance of knowing the variation of delay in addition to average delay and presents a simple approach to capture the variation in delay. </p

Methods to enhance the quality of bi-level origin–destination matrix adjustment process

The quality of Origin–Destination matrix (OD) estimation depends on number of factors including the selection of appropriate upper-level function of bi-level formulation, constraints to the OD flows, and suitable solution algorithm. Addressing these aspects, the study first explored different upper-level formulations using two types of traffic information: traffic counts and sub-path flows. Second, it investigated the effects of OD constraints on the quality of solution. Third, it proposed modified genetic algorithm (MGA) to address the computational limitations of traditional genetic algorithm (GA). The study findings were as follows: a) Using symmetric mean absolute percentage error (SMAPE) to match traffic counts showed greater improvements in the OD quality; b) The estimates improved as more number of OD pairs were known to have a-priori knowledge about their flows with higher confidence levels; c) The MGA approach outperformed GA in terms of computational efficiency, and gradient descent (GD) in terms of solution quality.</p