Equilibrium analysis and route guidance in large-scale networks with MFD dynamics

Recent studies have demonstrated that Macroscopic Fundamental Diagram (MFD), which provides. an aggregated model of urban traffic dynamics linking network production and density, offers a new generation of real-time traffic management strategies to improve the network performance. However, the effect of route choice behavior on MFD modeling in case of heterogeneous urban networks is still unexplored. The paper advances in this direction by firstly extending two MFD-based traffic models with different granularity of vehicle accumulation state and route choice behavior aggregation. This configuration enables us to address limited traffic state observability and to scrutinize implications of drivers' route choice in MFD modeling. We consider a city that is partitioned in a small number of large-size regions (aggregated model) where each region consists of medium-size sub-regions (more detailed model) exhibiting a well-defined MFD. This paper proposes a route guidance advisory control system based on the aggregated model as a large-scale traffic management strategy that utilizes aggregated traffic states while sub-regional information is partially known. In addition, we investigate the effect of equilibrium-conditions (i.e. user equilibrium and system optimum) on the overall network performance, in particular MFD functions. (C) 2015 Elsevier Ltd. All rights reserved

Joint estimation of paths and travel times from Bluetooth observations

Bluetooth data sets allow a direct estimation of travel times across sensor pairs; however, the resulting estimations contain noise because of missed detection rate and alternative paths between sensors. Additionally, although Bluetooth data sets allow tracking of vehicles across sensors, they do not provide an exact path (i.e. a sequence of traffic sections). Estimating vehicle paths from Bluetooth records is not a straightforward task. This paper proposes a joint method to simultaneously infer vehicle paths and travel times using Bluetooth records. The methodology is applied in a case study of Bluetooth measurements in Brisbane, Australia. The results are validated against travel time observations, which are collated by creating a testing set out of the whole data set. Travel time estimates are robust to the changes in the size of the available measurements, and the proposed model significantly outperforms a naive model where travel times are estimated via direct matching

Observing and reconstructing aggregated dynamic route choice patterns for large-scale mixed urban/freeway networks

This paper observes aggregated route choice patterns (i.e. vehicle distance traveled in different roadway classes, regional split ratios and average trip lengths) in a large scale mixed urban/freeway system through an extensive data set of 20,000 taxis in Shenzhen, China. It also reconstructs the aggregated patterns through shortest path algorithm that is based on various travel cost functions. We replace each observed trajectory with a shortest path that connects the same origin and destination points, and reproduce aggregated variables. We observe that link-level and regional interpretation of travel cost results in similar aggregated patterns. These results can enhance parsimonious network models and lead to better traffic predictions for large-scale congested networks

Identification of communities in urban mobility networks using multi-layer graphs of network traffic

This paper proposes a novel approach to identify the pockets of activity or the community structure in a city network using multi-layer graphs that represent the movement of disparate entities (i.e. private cars, buses and passengers) in the network. First, we process the trip data corresponding to each entity through a Voronoi segmentation procedure which provides a natural null model to compare multiple layers in a real world network. Second, given nodes that represent Voronoi cells and link weights that define the strength of connection between them, we apply a community detection algorithm and partition the network into smaller areas independently at each layer. The partitioning algorithm returns geographically well connected regions in all layers and reveal significant characteristics underlying the spatial structure of our city. Third, we test an algorithm that reveals the unified community structure of multi-layer networks, which are combinations of single-layer networks coupled through links between each node in one network layer to itself in other layers. This approach allows us to directly compare the resulting communities in multiple layers where connection types are categorically different

Travel time estimation and prediction in closed toll highways

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, and the demand level associated with particular OD pairs is not always enough to obtain accurate average travel times. Therefore, two estimation methods based on universal kriging and mathematical programming are proposed to estimate single section travel times using vast amount of available data from the electronic toll collection system of NJ Turnpike. To be valuable, travel time information must be updated continuously in real-time to provide not only estimates of current traffic conditions but also future projections. Time series models are commonly used in transportation area to obtain future traffic states. This thesis compares the prediction performance of a parametric model, ARIMA, a recently developed non-parametric model, SVR, a commonly used non-parametric model, ANN, and tests their performances under both typical and atypical traffic conditions.M.S.Includes bibliographical referencesby Mehmet Yildirimogl

How far is traffic from user equilibrium?

The interplay between road infrastructure, traffic conditions and travel choices is in the core of any infrastructure initiative, traffic control strategy or policy change. While the traditional traffic analysis framework assumes full awareness of travel costs and relies on the definition of user equilibrium state, there are few studies in the literature that test the existence of such equilibrium state. This study evaluates the widely applied shortest path assumption and so user equilibrium state from two aspects; (i) user perspective: similarity between actual and shortest paths, (ii) network perspective: node loads that result from actual and shortest paths. This study uses the GPS trajectory data set of approximately 20,000 taxis from Shenzhen, China to estimate the travel costs and reveal the actual routes followed in the network. It also considers two types of shortest paths that are based on free flow and estimated travel time. User perspective analysis concludes that most travellers do not choose the shortest path based on neither free flow nor estimated travel time. On the other hand, network perspective examination demonstrates similar network-wide patterns with actual routes and shortest paths based on estimated travel time

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or 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 fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. 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

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