Seyed Amir H Zahabi

Estimating Potential Effect of Speed Limits, Built Environment, and Other Factors on Severity of Pedestrian and Cyclist Injuries in Crashes:

Road facilities in urban areas are a major source of injury for nonmotorized road users despite the benefits of nonmotorized transportation. In particular, large Canadian cities such as Montreal face serious problems with pedestrian and cyclist safety. To address these problems, funds are continually allocated through different safety improvement programs such as reduction of speed limits, improvement of intersections, and increased traffic enforcement. However, few analytical tools help to identify and quantify the benefits of countermeasures (e.g., roadway design, speed management strategies, or land use policies) in reducing accident frequency and severity. Injury severity models were developed to determine the effects of road design, built environment, speed limits, and other factors (e.g., vehicle characteristics and movement type) on injury severity levels of pedestrians and cyclists involved in collisions with motor vehicles. Sources of data included police reports describing vehicle–pedestrian and vehicle–cyclist collisions, as well as information on land use, transit network, and road design attributes from the city of Montreal. The impacts of road design, land use, built environment, and other strategies on the injury severity levels of vulnerable road users were investigated. Factors such as darkness, vehicle movement, whether an accident occurred at an intersection, vehicle type, and land use mix affected the severity of pedestrian injuries from collisions. For cyclists, however, only vehicle movement and whether the accident occurred at a signalized intersection had significant effects on the severity of the injury.

Modeling the effect of land use on activity spaces

Historically, research that has analyzed the effect of land use on travel demand has concentrated on a few key indicators, notably, mode choice, vehicle miles traveled, and number of trips. This literature has focused primarily on the effects of individual land use variables; for example, what is the effect of land use mix or population density on mode choice? It is becoming increasingly clear, however, that the isolated impact of particular measures of land use on individual and household transportation behavior is small, but when these measures are dealt with by using a clustered approach, their combined influence becomes less ambiguous in direction and greater in magnitude. This paper contributes to the transportation and land use literature by examining the effect of clusters of land use indicators on activity spaces, an emerging but traditionally ignored indicator of transportation behavior. Regression analysis results point to a significant relationship between large and dispersed activity spaces, low levels of population and employment density, and low levels of public transit accessibility and land use mix.

Simultaneous Modeling of Endogenous Influence of Urban Form and Public Transit Accessibility on Distance Traveled

This paper describes an attempt to understand better the endogenous relationships between urban form, accessibility to public transit, and daily travel distance. A model of two simultaneous equations was implemented. The model took into account the interaction between the ownership of vehicles and the choice of household location as explanatory endogenous variables for total distance traveled by respondents. Choice of household location was defined on the basis of cluster analysis (neighborhood typology) driven by land use mix, population density, and accessibility to transit. With socioeconomic variables controlled for, the impacts of neighborhood typologies combined with car ownership levels as endogenous choices were estimated with the use of a model with simultaneous equations. This research used data from the Quebec City, Quebec, Canada, origin–destination survey conducted in 2001. The data set included responses from more than 50,000 individuals. Among other results, the presence of endogeneity was confirmed. When endogeneity was not taken into account, the joint effects of car ownership and household location choices were underestimated. According to the model with simultaneous equations, the total distance traveled by individuals was primarily influenced by employment status and household structure. In fact, the total distance per individual had an average rate of growth of 50% when the individual was working full-time. The distance also increased by 5.7% per child and decreased by 2.4% per person. Although the elasticities of urban form and transit supply variables introduced individually into the model were small, the elasticities of neighborhood type as endogenous variables were much more relevant.

A Spatial and Temporal Comparative Analysis of the Effects of Land-Use Clusters on Activity Spaces in Three Quebec Cities

Previous literature on transportation and land use has focused on the effect of individual land-use variables, such as population and employment density, and on measures of transportation demand, such as vehicle kilometers traveled and mode split. In contrast, our work uses activity spaces, a relatively unexplored measure of travel dispersal, as a dependent variable and neighborhood clusters to capture the effect of land use on this variable. This paper is an extension of previous research that dealt with Montreal exclusively and similar methods are used to compare three cities (Montreal, Quebec City, and Sherbrooke) over multiple years (1998–2008). We control and tests for the possibility of residential location self-selection bias through simultaneous equation modeling. The main findings are that (i) activity spaces are clearly linked to land use (through neighborhood clusters), as well as to overall city size; (ii) activity spaces appear to be growing over time where employment centers are fixed; and (iii) exogeneity in explanatory variables cannot be rejected.

Urban Transportation Greenhouse Gas Emissions and Their Link with Urban Form, Transit Accessibility, and Emerging Green Technologies: Montreal, Quebec, Canada, Case Study

A greenhouse gas (GHG) emissions inventory is estimated at the household level from disaggregated trip data considering all emitting modes. Trip-level GHG emissions are estimated by combining data sources (e.g., origin–destination surveys, vehicle fleet characteristics, transit rider ship data) and by using modeling tools (traffic assignment and GHG models) developed for Montreal, Quebec, Canada. A simultaneous equation model framework is implemented to investigate links between urban form, transit supply, sociodemographics, and travel GHGs, taking into account the issue of residential self-selection. The potential impacts of land use and transit supply strategies with emerging green technology scenarios are then compared with each other. Findings are consistent with the literature; built environment attributes are statistically significant (10% increase in density, transit accessibility, and land use mix results in 3.5%, 5.8%, and 2.5% GHG reductions, respectively), and the number of workers and retirees make important contributions to GHG emissions at the household level (102% increase from adding one worker and 51% decrease from adding one retiree). Also, if the current transit fleet were replaced with electric trains and hybrid buses, transit GHGs would decrease by 32%. If current trends persist in the private motor vehicle fleet, continued improvements in car fuel economy are estimated to reduce car GHGs 7% by 2020. The two most effective strategies for reducing regional and household GHGs appear to be to improve the fuel efficiency of the private motor vehicle fleet and to increase transit accessibility.

Transit Trip Itinerary Inference with GTFS and Smartphone Data

Many emerging technologies have been developed to supplement and contribute to conventional household travel surveys for transport-related data collection. A great deal of research has concentrated on the inference of information from global positioning system (GPS) data and data collected from mobile phones; methods for inferring transit itinerary have not received much attention. Automatic detection of transit itineraries from smartphone travel surveys could be used by planning agencies to predict transit demand and help in analysis of transit planning scenarios. This paper describes a proposed approach to infer transit itinerary smartphone travel survey and general transit feed specification data from Montreal, Quebec, Canada. Transit trips from the 2013 household travel survey were recreated and recorded with the DataMobile smartphone travel survey from May to July 2016. Transit itineraries were then validated—that is, collected data were associated with transit routes for all parts of the trips. The proposed transit itinerary inference algorithm was then applied to these validated data. The approach relied on the notion of transit route ambiguity—that is, because transit routes can overlap, any attempt to associate GPS data with routes when routes do overlap will result in ambiguity in identifying which routes were actually used. The proportion of transit trips with associated transit routes that were ambiguous was calculated under different assumptions, rules, and eventually a simple algorithm. Findings indicate that, by using this approach, 94.2% of transit trip distance can be assigned to either one transit route or walking, and thus there is reduced ambiguity. This resulted in 87% correct prediction of transit routes.