Hanna Maoh

Toward sustainable urban mobility: Investigating nonwork travel behavior in a sprawled Canadian city

ABSTRACT The prevalence of urban sprawl, amplified levels of auto dependency, and nonwork travel during the afternoon peak hours have resulted in a steady increase in unsustainable patterns of travel in Canadian cities. Active and green modes of transportation along with smart growth have been promoted as a panacea. This article investigates the efficacy of such sustainable urban mobility strategies in Windsor, Ontario, using data records from a Household Travel Survey. Multinomial and mixed-logit models are developed to identify the factors influencing nonwork mode choice travel behavior. Next, the models are used in a scenario-building and simulation exercise to illustrate the benefits attained from jointly improving public transit, encouraging smart growth development, and lowering vehicle ownership. Based on the obtained results, embracing single-policy instruments is not an effective approach for reducing auto dependency in the study area. However, adopting a multidimensional policy approach that integrates land use and transportation policy instruments is proven more effective for achieving sustainable outcomes. It is recommended that the service and facilities for transit and nonmotorized modes be improved, and easier access to commercial and recreational activities be enhanced through progressive improvements to the built environment. Adoption of smart-growth strategies should be also pursued by planners and decision makers to create an environment conducive to reducing the levels of auto ownership. Such a goal is of paramount importance for sustainability because the level of auto ownership emerged as the most important policy instrument for reducing auto dependency in the study area.

Characterization of International Origin–Destination Truck Movements Across Two Major U.S.–Canadian Border Crossings

In 2013, 3.9 million trucks crossed between the United States and Canada at the Ambassador Bridge or the Blue Water Bridge. These trucks accounted for 43.1%, by value, of all truck trade between the two countries. GPS pings that tracked the movement of Canadian-owned trucks over a span of 1 year were used to characterize freight activities at the two Canada–United States border crossings. (A “ping” is a GPS data record that identifies the location of a subject at a given point in time. The term is commonly used in the information technology and communications industries.) A total of 172,000 and 82,000 crossing events were identified and analyzed at the Ambassador Bridge and the Blue Water Bridge, respectively. This paper describes the development of origin–destination data pertaining to truck trips that utilize the two border locations. The paper also includes an estimation of the industries involved in individual trips, on the basis of those trips’ start and end locations. The combination of origin, destination, crossing time and location, and industry provides an immense amount of information on the nature of international truck movements at the Canada–United States border. The resulting spatial patterns provide evidence that the two border crossings are used for both short- and long-range trips that include locations on the West Coast and the southern U.S. border. Although crossing times did not vary considerably by industry, they were influenced by the distance of the trip: short-distance journeys had shorter crossing durations, on average.

Emissions and built form – an analysis of six Canadian cities

Abstract A direct result of population growth in large cities is an increased demand for public transit, goods movement, and road transportation. This increase in transportation demand consequently results in increases in mobile source emissions. Given the large amount of greenhouse gas (GHG) emissions produced by the Canadian resource industries, Canadian cities must rein in mobile source pollution if the country is to improve its status on the world stage. This study addresses this issue through presenting a Canada-specific framework for estimating mobile source emissions at the citywide level. Several air pollutants – including CO2, NO2, particulate matters (PM2·5 and PM10), and air toxics – are considered in this exercise, and summarized using a principal component analysis (PCA). Aggregate emissions are compared to various built form and travel behavior attributes for each respective city. The results indicate that population, travel to work behavior, and various built form attributes indeed impact transportation emissions at the citywide level.

Location and Transportation Effects on Nonresidential Real Estate Price Regressions in Windsor, Ontario, Canada

In this paper, land prices for nonresidential real estate listings in Windsor, Ontario, Canada, are analyzed with the use of ordinary least squares regression models. Location and transportation attributes are used in the analysis to explain observed land prices. Seven models were estimated to control the heterogeneity in the land use types. The results show differences in factors explaining land prices of the models, indicating that caution should be used when land use types are aggregated together. The role of transportation carried mixed results. Rail had a positive effect for industrial properties but was negative for commercial and food services. Transit had a positive effect on vacant land. Direct proximity to highway ramps had a negative correlation for vacant land, but indirect variables such as potential (residential) accessibility and time to the central business district indicated the positive effects that roads and highways had. Testing showed that although spatial autocorrelation was present in the price data, the independent regressors used in the modeling partially mitigated that effect. Multicollinearity and heteroscedasticity were also accounted for throughout the modeling process. The results obtained provide a useful account of various spatial and transportation-related phenomena for a midsize Canadian metropolitan area. Moreover, the empirical analysis is particularly valuable given the lack of modeling done on the commercial and industrial prices when compared with that done for residential properties.

Simulating Sustainable Urban Gateway Development: Illustration from Hamilton, Ontario, Canada

The term “gateway” refers to a city, or to some transport and logistics-oriented area in a city, that is associated with goods movement in, out, and through the area. Although the definition of a gateway is typically focused on goods movement, a more holistic view is adopted with consideration of the movements of people and the environmental implications of all movements. The relevance of this view is based on the interdependence of commercial and persons mobility, because all moves are happening within the same transport network, and is based on considerations of quality of life in a gateway city. Hamilton, Ontario, Canada, was chosen to test these concepts. Hamilton had a suitable geographical location, a busy port, an international airport, good highway and railway access, and an educated labor force. The gateway prospects for Hamilton were given perspective through a study of other prominent gateways that distilled success factors. Analytical work focused on multiregional economic impact modeling to assess the direct and indirect effects of Hamiltons potential evolution as a gateway. Local-level analysis, through integrated urban modeling and simulation of scenarios, stressed the impact of gateway development on commercial goods movement, auto commuting levels, emission levels, and transit ridership. Increased emissions resulting from gateway economic development could be overcome with forward-thinking policy focused on the uncongested movement of goods and people, compact urban form, and enhanced public transit working in concert. The addition of light rail transit in Hamilton and the promotion of a compact urban form would be catalysts for sustainable local gateway development. Finally, gateway-oriented development in Hamilton will cause noticeable regional economic growth. The models that were developed can be calibrated for other cities, given appropriate data.

Border Crossing Choice Behavior of Trucks Along Trade Corridor Between Toronto, Ontario, Canada, and Chicago, Illinois

Canada and the United States are heavily dependent on a few border crossing locations to facilitate international trade between the two countries. These crossings cost firms extra time and uncertainty in their supply chains that reduce the effectiveness of trade. Therefore, a great need exists to study the movement of vehicles traversing these border locations and identify characteristics that make these locations attractive. To this end, this study utilizes GPS data to study trucks traveling between the transportation hubs in Toronto, Ontario, Canada, and Chicago, Illinois. Two feasible choices are available for these trucks to cross the international border: Blue Water Bridge and Ambassador Bridge. Although most route choice decisions are based primarily on reducing travel time, a unique aspect of this corridor is that the total trip time by route is relatively even. A logit model was estimated to identify other factors that may influence the route choice decisions of trucks in the Toronto–Chicago corridor. The results suggest that a higher average crossing delay for a given time of the day has a negative influence on the selection of a given border crossing. Such an effect is more pronounced for the Blue Water Bridge. Other factors influencing the choice of crossing include the type of industry served by the truck, the carriers operating the trucks, time of the day, and day of the week.

The Impact of Public Transport Infrastructure on Residential Land Value: Using Spatial Analysis to Uncover Policy-Relevant Processes

This chapter reports the findings of a recent study, which aims to quantify the land value benefits of public transport infrastructure. The empirical investigation focuses on central Cardiff, Wales, where data comprising approximately 9000 property sale records were collected in the period 2000–2009. Through an exploratory and explanatory spatial data analysis, it was found that: (a) there is an underlying bus stop-oriented spatial dependence of the values of residential land, which varies based on the types of housing property (e.g. detached house, semi-detached house, flat) across different areas in Cardiff; (b) after controlling for spatial dependence, there is still a significant positive correlation between bus stops and change in land value. Most importantly, different types of property seem to benefit differently from land value uplift due to bus stop locations. The highest benefits are identified for flats and semi-detached homes followed by terraced and detached homes. The corresponding price gains range from 1.4% for flats to 0.7% for detached homes.

Factors Influencing Journey-to-Work by Public Transit in Mega Canadian Cities

In this chapter, a modeling exercise is carried out to analyze the factors influencing the journey-to-work by public transit in the largest two Canadian urban centers: Toronto, Ontario and Montreal, Quebec. Transit trip generation at the census tract level for the year 2011 is regressed against urban form and network design variables after controlling for several socio-economic and demographic variables. The simultaneous auto-regressive (SAR) modeling approach is employed to account for the presence of spatial dependencies in the modeled data. These spatial effects are usually neglected in travel demand models, leading to bias in the estimates obtained. While the estimated socio-economic and demographic parameters reinforce previous findings from the literature, the inclusion of urban form and network design variables offers new insights regarding the role that urban planning can have on transit trip generation. The results reported in this chapter can, thus, help promote sustainable urban planning policies.

Examining the Variability of Crossing Times for Canadian Trucks at the Three Major Canada–U.S. Border Crossings

Land borders connecting Canada and the United States are vital transportation facilities for the two countries. Truck crossing times at these facilities can have a significant impact on the performance of the economy. To date, knowledge about border crossing times has been limited due to lack of detailed data on the Canadian border. This article explores and models the patterns of crossing times at the three major land crossings connecting Canada to the United States: Ambassador Bridge, Blue Water Bridge, and Peace Bridge. The analysis is based on 387,775 border crossing truck trips that were generated between Canada and the United States over a course of twelve months. Seemingly unrelated regression (SUR) models are estimated to evaluate the seasonal and hourly crossing times of Canada- and U.S.-bound trips on each border crossing, controlling for traffic intensity in the models. The SUR modeling approach is chosen to control for potential cross-model correlations. The results suggest that crossing times at the border vary by season and hour of the day. Crossing times also vary by direction of traffic and by type of day (i.e., weekday vs. weekend). Traffic intensity has a significant influence on crossing times at two of the crossings but not the Blue Water Bridge. Finally, crossing times are more variable during the summer season and tend to be higher during the late evening hours and past midnight.

Modeling the Demand for Renting Electric Vehicles in Canada: A Stated Preference Choice Approach

The introduction of electric vehicles (EV) is considered by many as an effective solution to alleviate petroleum dependency; however, the number of EVs in the current market remains scarce despite of its potential benefits. Many studies have been conducted to identify and assess various factors that significantly affect EV ownership. In contrary, little has been done regarding the potential EV adoption for commercial fleets. This paper addresses this limitation by focusing on rental vehicles. It is found that rental cost, vehicle attributes (e.g. maximum range and recharging time), and attitudinal statements have significant effect on EV rental choice.