Ali Zockaie

Planning charging infrastructure for plug-in electric vehicles in city centers

ABSTRACT Installing charging facilities in existing parking lots in city centers is considered an effective measure to encourage the adoption of plug-in electric vehicles (PEV). This article is concerned with the problem of locating these facilities so as to minimize the total system cost. The problem is formulated as a fixed charge facility location model with charging capacity constraints. The proposed model extends the existing work by allowing unserved demands and considering drivers’ preference for familiar parking lots. Accordingly, inconvenience costs are introduced for unserved demands and for those who have to change their parking lots in order to charge at work. The proposed model is not only always feasible, but also introduces a pricing mechanism so that the level of service (measured by the cost of unserved demands) can be traded off with the infrastructure cost. In addition, a stochastic version of the proposed model is developed to address the effects of uncertain PEV market penetration rate on the planning of charging facilities. A case study is conducted to analyze the sensitivity of the models to different cost components, and to compare the results of the stochastic and deterministic models.

Estimating Network Fundamental Diagram Using Three-Dimensional Vehicle Trajectories: Extending Edie’s Definitions of Traffic Flow Variables to Networks

This paper evaluates measurement methods for traffic flow variables taken at the network level. Generalized Edies definitions of fundamental traffic flow variables along highways are extended for considering vehicles traveling in networks. These definitions are used to characterize traffic flow in networks and form the basis for estimating relationships among network density, flow, and speed in the form of a network fundamental diagram. The method relies on three-dimensional vehicle trajectories to provide estimates of network flow, density, and speed. Such trajectories may be routinely obtained from particle-based microscopic and mesoscopic simulation models and are increasingly available from tracking devices on vehicles. Numerical results from the simulation of two networks, in Chicago, Illinois, and Salt Lake City, Utah, are presented to illustrate and validate the estimation methodology. As part of the verification process, the study confirms that the traffic flow fundamental identity (Q = K · V) holds at the network level only when networkwide traffic flow variables are defined consistently with Edies definitions.

Implementation and Evaluation of Weather-Responsive Traffic Management Strategies

This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies.

Impacts of Correlations on Reliable Shortest Path Finding

This paper examines how correlations in link travel times affect reliable path finding in a stochastic network. The reliable path is defined as the path that requires the lowest travel time budget to ensure a given probability of on-time arrival. Such a path can be found by solving the shortest path problem considering on-time arrival reliability (SPOTAR). SPOTAR is solved approximately by using an approach based on Monte Carlo simulation. A major advantage of the simulation-based algorithm is its ability to deal with correlated link travel times. Through the use of a real-world network, the simulation-based algorithm is first validated by comparing it with a label-correcting algorithm that can solve the uncorrelated case exactly; the impacts of the correlations on link travel times are then examined. The results of the numerical experiments indicate that correlations affect the optimal SPOTAR solutions significantly. However, larger correlations do not always lead to larger errors in the reliable route choices that ignore them.

Definition and Properties of Alternative Bus Service Reliability Measures at the Stop Level

The Transit Capacity and Quality of Service Manual (TCQSM) provides transit agencies with tools for measuring their systems performance in different levels of operation. Bus service reliability as one of the key performance measures has become a major concern of both transit operators and users because it significantly affects users’ experience and service quality perceptions. The objective of this paper is to assess the existing reliability measures proposed by TCQSM and develop new reliability measures at the stop level. Utilizing empirical data from archived Bus Dispatch System (BDS) data in Portland, Oregon, a number of key characteristics of distributions of delay and headway deviation are identified and three new reliability measures at the stop level are proposed. The results of this study can be implemented in transit operations for use in improving schedules and operations strategies. Also transit agencies can use the proposed reliability measures in order to evaluate and prioritize stops for operational improvement purposes.

Solving Detour-Based Fuel Stations Location Problems

This article studies the problem of locating fuel stations to minimize the extra cost spent in refueling detours, which belongs to a class of discretionary service facility location problems. Unlike many studies of similar problems in the literature, the proposed model considers capacity constraints and compares different ways to incorporate them in the formulation. Note that ignoring the capacity constraint results in nonoptimal and unrealistic solutions. The proposed models are solved using both an off-the-shelf solver CPLEX and a specialized meta-heuristic method Simulated Annealing developed in this study. The solution methods are tested and compared in two case studies; a test benchmark network and a large-scale network. An effort to overcome the memory limitation of CPLEX through more compact formulation was partially successful: it results in a model that is less tightly bounded by its linear relaxation and hence is much more difficult to solve. In contrast, the Simulated Annealing algorithm scales better and is able to consistently yield high-quality solutions with a reasonable amount of computation time.

Dynamics of Urban Network Traffic Flow During a Large-Scale Evacuation

This paper explores some of the dynamics of urban network traffic flow during a large-scale evacuation in the context of the network fundamental diagram (NFD). The structure of the evacuation demand can significantly affect network performance. A radial-shaped structure results in a more stable network recovery compared with a directional evacuation structure. This study confirms the existence of unloading–reloading hysteresis when a network is subject to successive cycles of loading and unloading. If a network undergoes a complete or near-complete recovery, the reloading path in the NFD follows almost the same path as in the initial loading. Results suggest that the linear relationship between average network flow and trip completion rate does not always hold, as previously thought. The relationship becomes highly scattered and nonlinear when the network is highly congested and under disruption and the number of adaptive drivers is sufficiently large. Frequent route switching by adaptive drivers can artificially increase the average network flow but does not necessarily increase the network output (trip completion rate). Adaptive driving increases fluctuations in the NFD; however, it reduces hysteresis and gridlock while increasing network capacity.

Activity-Based Model with Dynamic Traffic Assignment and Consideration of Heterogeneous User Preferences and Reliability Valuation: Application to Toll Revenue Forecasting in Chicago, Illinois

To forecast the impact of congestion pricing schemes, it is essential to capture user responses to these schemes and the resulting dynamics of traffic flow on the network. The responses of users must include route, departure time, and mode choices. To capture the effects of these decisions, this paper lays out a framework for the integration of the relevant elements of an activity-based model (ABM) with a dynamic traffic assignment (DTA) model and a simulation framework to support the analysis and evaluation of various pricing schemes. In this paper, a multicriterion dynamic user equilibrium traffic assignment model is used; the model explicitly considers heterogeneous users who seek to minimize travel time, out-of-pocket cost, and travel time reliability in the underlying route choice framework. In addition to the methodological developments, various demand and supply parameters are estimated and calibrated for the selected application network (the Greater Chicago, Illinois, network). This paper showcases the integration of ABM components and a DTA in one coherent modeling framework for the implementation and evaluation of congestion pricing in an actual large-scale network.

Simulation-Based Method for Finding Minimum Travel Time Budget Paths in Stochastic Networks with Correlated Link Times

The aim of this study was to solve the minimum path travel time budget (MPTTB) problem, in which the travel time budget was the reliability index. This index was defined as the sum of the mean path travel time and the scaled standard deviation, which included the covariance matrix to consider correlation. Two existing solution methods in the literature, the outer approximation algorithm and Monte Carlo simulation method, were applied to solve the MPTTB problem. The former method approximated the hard nonlinear constraint of the MPTTB problem by a series of linear cuts generated iteratively and repeatedly solved a mixed integer program. The latter method, which was a simulation-based method, included two stages. The first stage founded a set of candidate paths, and the second stage generated the distribution of travel times for the existing paths in the candidate set. The numerical results for these two solution methods were conducted on the Chicago sketch network, and results showed that the methods found comparable solutions though they have respective advantages and drawbacks. Although the outer approximation algorithm demonstrated promising performance, it still relied on repeatedly solving a mixed integer program (subproblem) with a commercial solver, which could be a challenging task in its own right. The simulation-based method offers a good Plan B in the case in which other algorithms encounter difficulties.

Integrating Behavioral Models in Network Operations: Evaluating Traveler Information and Demand Management for Weather-Related Events

The main goal of the study was to integrate demand models into weather-responsive network traffic estimation and prediction system methodologies. The study examined the behavioral responses of travelers along several dimensions in response to weather-related transportation management strategies in conjunction with active travel demand management strategies before and during severe weather events. Specific management interventions included pretrip, information-based mode, and departure time choice adjustments, as well as policy-based rescheduling of school hours. The paper presents a case study of the Chicago, Illinois, area network under snow conditions to assess the effect of a combination of demand management strategies to maintain the same level of network performance as under clear weather conditions. A combination of earlier dissemination of information and school-opening policy resulted in a similar level of network performance maintained under a median snow day as compared with a clear weather day. The paper presents integrated supply and demand models for simulation and an assessment of demand management strategies in conjunction with weather-related congestion.