Mehdi Nourinejad

Investigation of Commercial Vehicle Parking Permits in Toronto, Ontario, Canada

As the City of Toronto, Ontario, Canada, implements stricter parking enforcement in the city’s downtown core, commercial vehicles (CVs) have become targets of increased ticketing and towing, often without alternate legal means of parking and loading. This paper investigates the feasibility of a CV parking permit to provide lawful and affordable parking options yet maintain a source of revenue for the municipality. Parking permits around the world are reviewed on the basis of their cost and scope. An analysis of historical parking citations in Toronto indicates clear patterns of parking behavior for which a permit would be beneficial. A nested choice model is developed to reflect the decision process of drivers searching for parking and calculate the revenue impacts of permit pricing schemes. This decision structure reflects a trade-off between permit pricing, legal parking costs (such as the value of walking time from distant loading zones), and the expected value of citations for illegal parking. The trade-off between permit revenue and parking ticket revenue shows that optimal permit pricing, in the order of Can

Network Learning via Multiagent Inverse Transportation Problems

300 annually, can provide an improvement in municipal revenue and achieve widespread adoption (Can

Extending Wardrop’s First Principle for Capacitated Networks

1 = US

A Mesoscopic Simulation Model for Airport Curbside Management

0.799 in March 2015). An improvement in social welfare is also achieved with permit adoption through the reduction of the cost of congestion, as permit holders are encouraged to park in legal zones away from congested arterials. The feasibility of a permit is contingent on the calibration of the price and rule structure in the fair appraisal of the value of parking in the downtown core and the needs of CV operators.

Developing a large-scale taxi dispatching system for urban networks

Despite the ubiquity of transportation data, methods to infer the state parameters of a network either ignore sensitivity of route decisions, require route enumeration for parameterizing descriptive models of route selection, or require complex bilevel models of route assignment behavior. These limitations prevent modelers from fully exploiting ubiquitous data in monitoring transportation networks. Inverse optimization methods that capture network route choice behavior can address this gap, but they are designed to take observations of the same model to learn the parameters of that model, which is statistically inefficient (e.g., requires estimating population route and link flows). New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers’ route behavior to infer shared network state parameters (e.g., link capacity dual prices). The inferred values are consistent with observations of each agent’s optimization behavior. We prove that the meth...

A dynamic carsharing decision support system

AbstractUser Equilibrium (UE) has been heavily studied during the last few decades, many studies of which define equilibrium flow of traffic for uncapacitated networks based on Wardrop’s first prin...

Truck parking in urban areas: Application of choice modelling within traffic microsimulation

Airport curbside congestion is a growing problem as airport passenger traffic continues to increase. Many airports accommodate the increase in passenger traffic by relying on policy and design measures to alleviate congestion and optimize operations. This paper presents a mesoscopic simulation model to assess the effectiveness of such policies. The mesoscopic simulation model combines elements of both microscopic simulation which provides a high level of detail but requires large amounts of data and macroscopic simulation which requires very little data but provides few performance measures. The model is used to simulate scenarios such as double parking, alternative parking space allocation, increased passenger demand, and enforced dwell times at Pearson International Airport in Toronto, Canada. Scenario analysis shows that adjusting model inputs provides reasonable results, demonstrating the value in using this approach to evaluate curbside management policies. The results show that double parking reduces the utilization ratio and the level of service of the outer curbside but cuts down the passenger and vehicle waiting time. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. Finally, reducing the allowable parking time at the curbside decreases the average dwell time of private vehicles from 89 seconds to 75 seconds but increases the number of circulating vehicles by 30%.

Agent based model for dynamic ridesharing

Taxis are increasingly becoming a prominent mobility mode in many major cities due to their accessibility and convenience. The growing number of taxi trips is cause for concern when vacant taxis are not distributed optimally within the city and are unable to find waiting passengers effectively. A way of improving taxi operations is to deploy a taxi dispatch system that considers the interrelated effects of taxis on other traffic modes. This paper presents a taxi dispatch model that takes into account the impact of taxis on normal traffic flows while optimizing for an effective dispatch policy. The presented model builds on the concept of the macroscopic fundamental diagram (MFD) to represent the dynamic evolution of the traffic conditions. A model predictive control approach is devised to control the taxi dispatch system on a two-region city case study. The results show that the case of no network-scale taxi dispatching leads to severe accumulation of taxi passengers and vacant taxis in different regions whereas the dispatch system improves the taxi service performance and reduces traffic congestion by regulating the network towards the undersaturated condition.