Sensing technologies for traffic flow characterization: From heterogeneous traffic perspective

Abstract

Importance of detailed traffic flow characterization is immense for achieving intelligent transportation system. As such, great efforts in existing literature have gone into proposing different solutions for traffic flow characterization. Among these, first generation intrusive sensors such as pneumatic tube, inductive loop, piezoelectric and magnetic sensors were both labor intensive and expensive to install and maintain. These sensors were able to provide only vehicle count and classification under homogenous traffic conditions. Second generation non-intrusive sensors based solutions, though a marked improvement over intrusive sensors have the capability to only measure vehicle count, speed and classifications. Furthermore, both intrusive and non-intrusive sensors based solutions have limitations when employed under congested and heterogeneous traffic conditions. To overcome these limitations, a compute vision methodology has been employed for traffic characterization under heterogeneous traffic conditions. The proposed solution was field tested on a complex road configuration, comprising of a two-way multilane road with three U-turns. Unlike both intrusive and non-intrusive sensors, the proposed solution can detect pedestrians, two/three wheelers and animal/human driven carts. Furthermore, detailed flow parameters such as vehicle count, speed, spatial/temporal densities, trajectories and heat maps were measured.

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

“…Mobility and traffic variables estimation Mobility data can be gathered from sources like surveys, in-situ sensor measurements [9], and activity proxies like geolocation data of individual people or vehicles. Lenormand et al [10] explored the connections between regular surveys, cell phone data, and Twitter (X) posts, and found potential biases in Twitter data related to user age.…”

Vehicular Flow Characterization: An Internet of Video Things-Based Solution

“…• Unlike intrusive\non-intrusive sensors, the proposed solution can operate under all (including congested and heterogeneous) traffic conditions. Furthermore, the proposed solution can measure more traffic flow parameters with easier installation and maintenance [22]. • Capability to provide detailed (eight) traffic flow parameters as compared to proposed solutions in existing literature such as [15,18,16,20,19].…”

Statistical Modeling of Traffic Flow in Commercial Clusters Based on a Street Network

“…(1) Two main methods are applied to capture traffic flow patterns in order to establish predictive analysis models. The first method, based on ultrasonic and infrared sensing, GPS, etc., records the number of vehicles passing through a certain segment in unit time as the traffic flow of that segment by transmitting the driving signal to the terminal through the vehicle [8][9][10]. The other is the prediction of traffic flow statistics based on images.…”