Mehmet Yildirimoglu

Multiple Concentric Gating Traffic Control in Large-Scale Urban Networks

A new gating strategy for concentric cities based on the notion of the macroscopic or network fundamental diagram and the feedback-based gating concept is introduced and successfully tested. Different regions of large-scale urban networks may experience congestion at different levels and times during the peak period. In this paper, the zone, including the initial core of congestion, is considered as the first region, which has to be protected from congestion via gating; eventually, as the congestion continues to expand, the border of an extended network part becomes the second perimeter for gating control. Remarkable extensions while distributing the ordered controller flow to the gated traffic signals in case of low demand or occurrence of spillback are also considered. A greater part of the San Francisco urban network is used as test-bed within a microscopic simulation environment. Significant improvements in terms of network-wide mean speed and average delay per kilometer are obtained compared to the single perimeter gating and non-gating simulation scenarios.

Modeling work zone crash frequency by quantifying measurement errors in work zone length

Work zones are temporary traffic control zones that can potentially cause safety problems. Maintaining safety, while implementing necessary changes on roadways, is an important challenge traffic engineers and researchers have to confront. In this study, the risk factors in work zone safety evaluation were identified through the estimation of a crash frequency (CF) model. Measurement errors in explanatory variables of a CF model can lead to unreliable estimates of certain parameters. Among these, work zone length raises a major concern in this analysis because it may change as the construction schedule progresses generally without being properly documented. This paper proposes an improved modeling and estimation approach that involves the use of a measurement error (ME) model integrated with the traditional negative binomial (NB) model. The proposed approach was compared with the traditional NB approach. Both models were estimated using a large dataset that consists of 60 work zones in New Jersey. Results showed that the proposed improved approach outperformed the traditional approach in terms of goodness-of-fit statistics. Moreover it is shown that the use of the traditional NB approach in this context can lead to the overestimation of the effect of work zone length on the crash occurrence.

Traffic signal perimeter control with multiple boundaries for large urban networks

A new gating strategy based on the notion of the macroscopic or network fundamental diagram (MFD or NFD) and the feedback-based gating concept is introduced and tested successfully. Different regions of large-scale urban networks may experience congestion at different times during the peak period. In this paper, the zone including the initial core of congestion is considered as the first region which has to be protected from congestion via gating; eventually, as the congestion continues to expand, the border of an extended network part becomes the second perimeter for gating control. Extensions while distributing the ordered controller flow to the gated traffic signals in case of low demand or occurrence of spillback are also introduced. A greater part of the San Francisco urban network is used as test-bed within a microscopic simulation environment. Significant improvements in terms of average travel time and average delay are obtained compared to the single perimeter gating and non-gating cases.

Experienced travel time prediction for freeway systems

Travel time is considered as one of the most important performance measures for roadway systems, and dissemination of travel time information can help travelers to make reliable travel decisions such as route choice or time departure. Since the traffic data collected in real time reflects the past or the current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow essentials (e.g. shockwave analysis, bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.

Comparison of real-time travel time estimation using two distinct approaches: Universal kriging and mathematical programming

Real-time estimates of traffic conditions are valuable information needed by operators of transportation facilities as well as travelers. This study aims to provide accurate travel time estimates using data collected by the electronic toll collection system instead of sensors and AVI readers specifically deployed for traffic monitoring. This dual use of toll readers for travel time estimation can be an attractive approach since it eliminates additional costs of deploying and maintaining sensors. However, this approach can present an important challenge in terms of accuracy of the estimates because readers are not located on the main roadway, but instead on the ramps. To break down trip travel times into section travel times, two methods based on, universal kriging and mathematical programming are developed. Our results based on the real-data show that it is possible to use electronic toll data to provide accurate estimations of link travel times especially using mathematical programming algorithm which outperforms universal kriging approach.

Searching for empirical evidence on traffic equilibrium

Cities around the world are inundated by cars and suffer traffic congestion that results in excess delays, reduced safety and environmental pollution. The interplay between road infrastructure and travel choices defines the level and the spatio-temporal extent of congestion. Given the existing infrastructure, understanding how the route choice decisions are made and how travellers interact with each other is a crucial first step in mitigating traffic congestion. This is a problem with fundamental importance, as it has implications for other limited supply systems where agents compete for resources and reach an equilibrium. Here, we observe the route choice decisions and the traffic conditions through an extensive data set of GPS trajectories. We compare the actual paths followed by travellers to those implied by equilibrium conditions (i) at a microscopic scale, where we focus on individual path similarities, and (ii) at a macroscopic scale, where we perform network-level comparison of the traffic loads. We present that non-cooperative or selfish equilibrium replicates the actual traffic (to a certain extent) at the macroscopic scale, while the majority of individual decisions cannot be reproduced by neither selfish nor cooperative equilibrium models.