Location-Based Data for Estimated Traffic on Urban Arterial in Heterogeneous Traffic Conditions

Abstract

Accurate estimation of traffic in intelligent transportation system applications, such as the advanced traveler information system and the advanced traffic management system, requires fixed location-based measurements, vehicle-based measurements, or both. Using both data sources is too expensive for most government agencies, especially in developing countries such as India, and also leads to issues related to installation and maintenance, especially on urban roads. The main drawback of vehicle-based measurements is the potential lack of participation because of privacy concerns; lack of participation would limit data collection to a sample of the population, primarily on public transport vehicles. The study aims to overcome such difficulties by using only location-based flow data for the estimation of spatial parameters, such as density and travel time. These parameters are difficult to measure or estimate on an urban arterial, especially under heterogeneous traffic conditions, because of lack of lane discipline and because of complex interactions among different vehicle types. The Lighthill–Whitham–Richards macroscopic traffic flow model discretized in both space and time was employed in the estimation scheme. The resulting partial differential equations were solved numerically with the finite difference formulation of forward–time backward–space. Both linear and exponential speed–density relationships were considered and incorporated into the macroscopic model. Linear and cubic spline interpolations of input flow values were compared. The estimated density was corroborated with the density obtained from input–output analysis. Estimated travel times were compared with manually observed travel times and travel times obtained from probe vehicles fitted with Global Positioning System devices.

A novel approach to model traffic on road segments of large-scale urban road networks

“…The rapid urbanization in developing countries has led to a marked leap in traffic volumes and caused several problems such as traffic congestion, road accidents, and air pollution [1,2]. Solving those problems has become a strong challenge due to high investment costs as well as the limited availability of accurate [[3], [4], [5]] and up-to-date data on traffic volume.…”

Effects of Data Characteristics on Bus Travel Time Prediction: A Systematic Study

“…In the first part of the analysis, a trajectory analysis was carried out to analyze the patterns in peak and off-peak trajectories and daily trajectories. In the previous studies made under similar traffic conditions [18], it was assumed that the peak and off-peak trip trajectories could be identified manually, and such groups of trips were significantly different from each other. Based on that, for the case with fixed clusters, trips between 8:00 am and 10:59 am and between 3:00 pm and 7:59 pm were considered peak trips and others as off-peak trips.…”

“…These overlaps observed in the trip trajectories raise the question of whether manual grouping can account for these highly varying travel time patterns. Previous studies from similar traffic conditions on the prediction of bus travel times [18,31] assumed that travel times followed weekly patterns. It was assumed that peak and off-peak timings remained constant over different weeks.…”

“…Kumar et al [17] analyzed trip-wise, daily, and weekly patterns of bus travel times and developed a bus arrival prediction model. Kumar et al [18] studied the travel time patterns for different days of the week using GPS data obtained from public transit buses. Another study by Vlahogianni et al [19] showed pattern-based prediction to be more accurate than the classical time-series approach for short-term traffic prediction.…”

Estimation of Missing Flow at Junctions Using Control Plan and Floating Car Data

“…Nakatsuji, T. et al (2004) estimate the turning movements at intersections using a logit-based stochastic user equilibrium (SUE) model integrated with a genetic algorithm. Kumar, S. et al (2011) use only location-based flow data to estimate some spatial parameters such as density and travel time by using LWR model. Banks, J.…”