Showing papers by "Lina Kattan published in 2021"

Modelling electric vehicle charging network capacity and performance during short-notice evacuations

Abstract: Electric vehicles (EVs) may add new challenges during mass evacuations. Understanding the magnitude of the impacts EVs may have during the pre-departure stage of mass evacuations is an essential first step when planning for mass evacuations in a future where EVs are more common. In this paper, a generalized framework based on a G/G/c/N queueing model (general arrival process, general service process, c charging stations, and N EVs) was developed to estimate the number of vehicles that can be charged in the pre-departure evacuation stage and thus assess the pre-departure impacts. The model outputs are the number of vehicles that have or have not been served during the evacuation period, as well as average queue times and maximum queue lengths. This model is tested using the current electric vehicle fleet and charging infrastructure of Prince George, British Columbia, as a case study with a hypothetical short notice forest fire scenario. It was found that for the present-day case of Prince George, there is not enough charging network capacity to service all vehicles before departure. Increasing the number of charging stations, providing earlier evacuation notices, and ensuring a balanced makeup of level 3 fast-charging of different types were all found to be effective in increasing the number of EVs that received adequate charging before departure.

Modeling the effect of sensor failure on the location of counting sensors for origin-destination (OD) estimation

Abstract: The network sensor location problem (NSLP) for origin–destination (OD) estimation identifies the optimal locations for sensors to estimate the vehicular flow of OD pairs in a road network. Like other measurement apparatuses, these sensors are subject to failure, which can affect the reliability of the OD estimations. In this paper, we propose a novel model that allows us to solve the NSLP for OD demand estimation by identifying the most reliable locations to install sets of sensors with consideration for a nonhomogeneous Poisson process to account for time-dependent sensor failure. The proposed model does not rely on the assumption that true OD demand information is known. We introduce two separate objective functions to minimize the maximum possible information loss (MPIL) associated with OD demand on sensor-equipped links and OD pairs during the lifetimes of the sensors. Both objective functions are formulated to incorporate the possibility of sensor failure into the calculated OD demands. We use stochastic user equilibrium (SUE) to address the stochasticity of traffic route selection. We then employ the weighted sums method (WSM) and an e-constraint to incorporate the objective functions into an integrated formulation. Two sensor types with different time-dependent failure rates are considered to identify the optimal locations for sets of sensors for OD demand estimation purposes while addressing the available budget constraints. We also address the problem of scheduled/routine maintenance of existing sensors by introducing an additional sensor deployment phase that focuses on maintaining the reliability of information by repairing or replacing failed sensors, installing additional sensors or a combination of both. The numerical results from the proposed model demonstrate how the deployment of more advanced sensors with lower failure rates can effectively improve the reliability of the information obtained from sensors. We also evaluate the use of different weights for the WSM’s objective functions to explore alternative combinations of sensor configurations. The introduction of additional sensors to a network shows that the decision between repairing failed sensors and installing new sensors is highly dependent on the available budget and the failed sensors’ locations.

Modeling Location Choice of Taxi Drivers for Passenger Pickup Using GPS Data

Abstract: Recently, the traditional taxi industry has been struggling to keep its market share, especially with the emergence of new transport network companies (e.g., Uber). One of the problems with traditional taxi services is the difficulty of matching the taxi demand to its supply when there is no phone-booking or other reservation system. From that perspective, the taxi driver?s experience is important in reaching the next passenger. A taxi driver with limited experience may not know the high-demand locations and times of taxi stands or street sections to visit after dropping off a passenger. This causes a large number of vacant taxi drivers to regularly cruise the roads to search for a passenger, contributing to congestion, pollution, and resource waste. We formulate the problem of a taxi driver?s next passenger pickup location as a destination choice problem. Vacant taxi trips between drop-off and pickup points are extracted from GPS records obtained from a taxi operator in Lisbon, Portugal, to understand the travel behavior of vacant taxi drivers. We have estimated destination choice models with a multinomial logit and a nested logit structure. It was found that passenger demand at the pickup area, hotspot locations, service location preference, and major transport hubs positively influence a taxi driver?s next choice of passenger pickup location. Results of this study provide insight regarding the factors that explain a taxi driver?s probability to choose a certain zone within a set of passenger pickup zones, contributing to a better understanding of taxi driver travel behavior.

Fair transit trip planning in emergency evacuations: A combinatorial approach

Abstract: This paper introduces the concept of proportional fair trip planning in the context of short-notice transit-based emergency evacuation. Proportional fairness attempts to meet social fairness among evacuees without sacrificing the efficiency of the evacuation process. The proportional fair trip planning concept is compared to the commonly used maximum safety concept that attempts to maximize the summation of safety functions of evacuees. We use a combinatorial approach to model the transit mass emergency evacuation in moving people from dangerous areas to safe shelters. High-density population and medium-density population variations of the problem are studied. For each variation of the problem, we study the computational complexity of the problem. We develop polynomial or pseudo-polynomial algorithms for each problem. Our numerical analysis shows that pure consideration of efficiency may result in highly unfair plans that only consider the portion of the population with the most payoffs (e.g., the population with the highest danger level) while ignoring the rest (potentially the vast majority of the population). While still considering efficiency, proportional fairness is shown to address this issue by also allocating resources to the population that has non-optimal payoffs.

Ramp metering control under stochastic capacity in a connected environment: A dynamic bargaining game theory approach

Abstract: This paper presents a dynamic predictive and cooperative ramp metering approach that considers stochastic breakdowns at merging bottlenecks. A stochastic microscopic model is used to estimate traffic state parameters based on speed, location, and travel time information from connected vehicles. Traffic state predictions are obtained on a lane by lane basis using an adaptive Kalman filter (AKF) that fuses fixed detector measurements with the model; the AKF then produces multiple step ahead predictions. The ramp metering problem in this paper is modeled as a stochastic distributed model predictive control (SDMPC) approach. The SDMPC problem is solved based on a bargaining game approach where each controller, a player in the game, receives traffic state and control decision information from other controllers to solve the local optimization problem based on expected local costs and constraints. The performance of the proposed model is evaluated for three aspects of efficiency: short-term and long-term equity and effectiveness compared to multiple control scenarios. The outcomes indicate that the proposed cooperative model with stochastic capacity considerations outperforms the deterministic capacity-based models in regard to effectiveness and equity properties. However, the centralized approach performs slightly better in respect to system-wide efficiency.

Transit Users’ Mode Choice Behavior During Light Rail Transit Short-Term Planned Service Disruption:

Abstract: Planned service disruptions (PSDs) of light rail transit (LRT) improve service reliability, extend infrastructure’s life, and reduce the frequency and impact of unplanned service disruption caused ...

A network-wide anticipatory control of an urban network using macroscopic fundamental diagram

Abstract: In this paper, a network-wide anticipatory control (AC) framework, incorporating drivers' route choice behaviour, is proposed The proposed AC, consisting of two main levels of control and route ch