Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic

Reenu George,Lelitha Devi Vanajakshi,Shankar C Subramanian

doi:10.1109/access.2019.2963273

Reenu George, Lelitha Devi Vanajakshi + Show 1 more

Open Access

https://doi.org/10.1109/access.2019.2963273

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 7	License type: CC BY 4.0

Affiliation: Indian Institute of Technology Madras

Abstract

Managing congestion in mixed traffic conditions, characterized by heterogeneous and lane-less traffic, is a challenging task. Traditionally density, defined as the number of vehicles in a road stretch, is used to quantify congestion. However, direct measurement of density is difficult and hence is usually estimated from other variables. In this paper, a relationship is derived between traffic density and area occupancy, a variable that can incorporate heterogeneity and lane-less movement. Using the derived density-area occupancy relation, a non-continuum macroscopic single state linear time varying model was developed. Estimation of density was done by using the Kalman filtering technique and corroborated with simulated density. The need for dynamic estimation is motivated by evaluating the performance of two static estimation schemes in the presence of uncertainties. Performance was tested for different traffic scenarios such as congestion and non-recurrent traffic incidents. Further, to improve the estimation accuracy in scenarios involving transitions in traffic conditions, an adaptive estimator was developed. It was found that the adaptive estimator provided the best estimation accuracy.

Highlights

AND BACKGROUNDMitigating traffic congestion to enhance the performance of transportation systems is a major challenge for traffic engineers globally
SUMMARY AND CONCLUSION Estimation of density is an essential component in the study of traffic congestion in mixed traffic conditions characterized by heterogeneous and lane-less traffic
This paper used area occupancy as a surrogate variable for traffic density estimation, which is capable of considering varying vehicle dimensions as encountered in heterogeneous and lane-less traffic

Summary

INTRODUCTION

Mitigating traffic congestion to enhance the performance of transportation systems is a major challenge for traffic engineers globally. Considering the above factors, a macroscopic non-continuum approach is adopted to estimate density (a discrete variable representing the number of vehicles in a section) under mixed traffic conditions. Reported studies in this area for heterogeneous conditions are limited and discussed below. Use of non-continuum macroscopic models for heterogeneous conditions based on the conservation law for vehicles and the fundamental relation of traffic flow was reported in the study by Anand et al [16]. Three approaches were reported in the early 1960’s, namely input-output technique, using fundamental speed-flow relationship and from percent occupancy [3] These static estimation schemes have their own limitations in calculating density, especially under mixed traffic conditions. Data were extracted from VISSIM for every 100 s, which is the time interval, h, used in this study

ESTIMATION SCHEME AND ITS EVALUATION

ADAPTIVITY TO DIFFERENT TRAFFIC SCENARIOS

Findings

SUMMARY AND CONCLUSION