Researchers from regional and transportation planning have focused a large body of research towards Integrated Urban Models (IUMs) and agent-based microsimulation of urban systems (Miller et al., 2004; Waddell, 2002; Hunt et al., 2005; Wegener, 1995). IUMs provide a simulation environment that can be used to support strategic planning by forecasting the effects of policies on demographics, land use changes, and transportation patterns within regions. Within urban systems, firms are recognized as one of the interacting agents whose behaviour influence transportation systems. Firm events of entry, exit, and growth affect economic growth, labour dynamics, and transportation demands. New firms are sources of new jobs that induce urban changes by attracting skilled workers to the location of new jobs. This causes changes to transportation demand and ultimately affects commuter time. Firm start-up size is an important determinant of new firms. Modelling firm start-up size at the micro level is essential to understand job creation dynamics and potentially measure the impact on transportation demand. Also, some studies suggest that firm start-up size influences the firm’s subsequent performance and survival (Dunne et al., 1989; Audretsch and Mahmood, 1994; Mata and Portugal, 1994). Determinants of firm start-up size have been addressed largely for European countries (Arauzo-Carod and Segarra-Blasco, 2005; Audretsch et al., 1999; Barkham, 1994; Görg et al., 2000; Mata and Machado, 1996). To the best of the author’s knowledge, research studies in a Canadian context that address determinants of firm start-up size are absent, both in terms of employment size or tangible assets. In this paper, ordered logit models of start-up size of Canadian firms are presented. Two aspects of size are considered: the number of employees and the firm’s physical form represented as the tangible asset dollar values. The presented models are the components of a larger firm microsimulation platform introduced earlier by Mostafa and Roorda, (2015). This paper starts by exploring determinants of firm start-up size surveyed from the literature. The data and modelling approach are then explained. Model results and interpretation followed by model goodness-of-fit and model validation are presented next. Concluding remarks and future directions are then discussed
Modern transportation planning dawned in New Brunswick in 1946 with the release of the “Master Plan of the Municipality of the City and County of Saint John, NB, Canada” at the time the 12th largest metropolitan area in Canada. This plan, authored by J. Campbell Merrett, was developed in anticipation of population growth, growth in automobile ownership and desire for modern housing in Saint John, trends which were all experienced in cities across North America over the coming decades. This plan, and others, ushered in an era of large scale highway investments that changed the complexion of New Brunswick’s largest cities throughout the 1960s through the 1980s. These projects were initially informed through transportation planning practice that focused on projecting vehicular traffic on a road network, typically within a municipal environment, with transit relegated to a social service role. More recent transportation planning efforts in New Brunswick’s largest cities have expanded the focus to include transit and active transportation, though the legacy of the highway megaprojects continues to shape urban mobility in New Brunswick. This paper briefly explores the progression of transportation planning in New Brunswick’s three largest urban areas (Saint John, Greater Moncton (Moncton, Riverview and Dieppe) and Fredericton) through a review of historic plans retained at the Harriet Irving Library at the University of New Brunswick. More recent plans were sourced online from municipal websites. The plans were reviewed chronologically in terms of their overall vision, technical approach, and contributing legacy to urban mobility in New Brunswick. Plans are compared and contrasted and a final commentary provided based on the perceived ability of New Brunswick cities to leverage transportation planning tools to address present and future challenges for continued reliance on car-centric mobility, such as an aging population and climate change.
The well-known triple bottom line breaks sustainability down into economic, environmental and social dimensions. According to Vantuono (2016), the triple bottom line “in railroading terms translates to a safety culture and highly competitive wages and benefits (people); fuel efficiency and far less pollution compared to other modes (planet); and shareholder value (profit).” On a ton-mile basis, rail is known to compare favorably vis-à-vis trucking in terms of fuel consumption and GHG emissions. In highly competitive markets, motor and rail carriers have been developing sustainability initiatives focused on reducing fuel consumption and GHG emissions, along with increasing operational efficiency (Shacklett 2012). Though fuel consumption and GHG emissions are prominent among the GRI (2013) sustainability reporting guidelines, surprisingly these items did not make a more recent top 10 list of GRI “sustainability aspects for the railroad sector” (Coppola 2014). This list includes: local communities, remediation, freedom of association and collective bargaining, diversity and equal opportunity, equal remuneration for women and men, non-discrimination, indirect economic impacts, labor/management relations, corruption and product and service labeling. This paper analyzes and critiques the sustainability reports published by Canada’s Class I railroads. There are four more sections. The first one briefly profiles the Canadian railroading industry. This is followed by a definition of sustainability, which expands the typical triple bottom line (of economic, environmental and social dimensions) by adding a cultural dimension. The third section introduces sustainability reporting, based on the Global Reporting Initiative guidelines, then uses the guidelines to study the Class I railroad sustainability reports. The fourth and final section offers conclusions and suggestions for future research.
Organized volunteer driver programs are emerging as solutions to fill the transportation service gap for those unable to meet their personal transportation needs independently with the private automobile and where taxi, transit or active transportation are unrealistic or unavailable options. Volunteer driver programs (VDP) are typically able to serve areas of low population density at a lower overall cost than paratransit services by using volunteer labour and vehicles. They replicate the on-demand travel and social aspects associated with relying on friends and family for transportation, which is attractive to those who do not have access to a personal network. VDP can be stand-alone programs, extensions of non-profit or charitable activities, or in some instances in the United States, are integrated as part of rural transit. Many of these programs are targeted to supporting the transportation needs of older adults, the population of which is expected to double in Canada by the year 2036 (FCM, 2011). The challenge is that little is understood about how VDPs collectively work to satisfy Canadian transportation needs and how they will respond to meet growth in ridership anticipated with an aging population. Individual programs may record trip information for their own planning purposes, but unlike personal vehicle use, transit, or taxis, there is not a broad understanding of the number of Canadians that rely on these programs, the degree of their reliance, the types of trips they take, and distances they travel. Without a clear understanding of how VDP work, there are risks that programs may not be able to meet demand, programs may have challenges with replication and sustainability, and there may be missed opportunities to employ these programs in underserved markets. This paper summarizes recent efforts to quantify the broader usage of these programs in New Brunswick. While confounded by differing data collection and reporting approaches among groups, this paper offers a preliminary estimate of usage, as well as the results of an exercise to develop a common reporting tool.
In transit planning, accessibility “to” (i.e. access to transit) and “through” transit (i.e. geographical coverage of transit) are considered as important service quality indicators (Beimborn et al., 2003; Handy and Clifton, 2001; Murray and Wu, 2003). Typically, travel time (or distance) is used as a measure for accessibility “through” transit. In recent studies, researchers pointed out that transit fare could be an obstacle to accessibility. For example, El-Geneidy (2016) suggested that travel cost (i.e. the transit fare one pays) would also influence accessibility through transit. In addition, a more recent study suggested that an increase in transit flat fare would result in a loss in accessibility. Such loss was found to be inversely proportional to the length of the trips (i.e. substantial for short trips and unworthy for long trips), which can be considered “unfair” for short-trip users (Ma et al., 2017). This study expands on Ma et al. (2017) and constructs a fair-fare structure for improved passengers’ accessibility. It is assumed that a transit agency needs to introduce a new fare policy that will cover its increasing capital and operating costs. A pre-determined loss of accessibility is set for all short, medium, and long trips; and a new fare structure is established accordingly. The City of Kelowna, BC, is selected as a case study.
Sustainability reporting is an effectual way for airlines to communicate their sustainability strategies and achievements. In spite of the existence of sustainability reporting guidelines such as GRI, airlines have shown a wide range of performances in reporting quality. To figure out the driving forces behind airlines’ sustainability reporting performance, this paper first builds a scoring system for airlines’ sustainability reports, with which the top 100 airlines worldwide are evaluated and scored. Then we run regression analysis to gauge the impacts of factors that may have influence on this score, including the size, the nationality, the business model (full-service vs. low-cost), the alliance membership, etc.
Transportation infrastructure is a major component of municipal budgets. A sample of financial statements from four Ontario municipalities of varying jurisdiction shows that transportation costs can account for 15-40% of a municipality’s operating budget (Baker, Jackson, KPMG, 2016; Deloitte LLP, 2016; KPMG, 2016; St Amant, Rossini, Wallace, PricewaterhouseCoopers LLP, 2016). Municipalities employ key performance indicators to track the fiscal sustainability of their spending, but many of these indicators come with caveats that prevent direct comparisons. Infrastructure, population and the physical supply of infrastructure are often not included together in a single indicator, making it difficult to track trends where only one of these factors changes, and make insights into the fiscal sustainability of municipalities. This research studies two facets of fiscal sustainability. First, an indicator is proposed that combines infrastructure supply, population, and expenditures, with the goal of enabling sustainability analyses that make use of one or more of these factors. Second, a sustainability analysis is performed for the road and transit networks in the City of Waterloo, Ontario, at the local and regional level. The proposed indicator is evaluated against a series of existing indicators covering 25 years of transit funding, and 11 years of road funding, ending in 2015. By covering over a decade of spending history, insights may be made into the long-term sustainability of transportation infrastructure in Waterloo.
Modelling the Feasibility of Transitioning Diesel-Based Heavy-Duty Trucks to CNG-Powered Engines in the GTHA Heavy-duty trucks are commonly used in freight transportation due to their ability to transfer high volumes of goods. Population growth and globalization increases traffic volume of all types of vehicles, which can lead to congestion. The increase in heavy-duty trucks is of greater environmental concern as most of these vehicles are powered by diesel and on average travel longer distances. The latter is associated with higher quantities of harmful emissions. Exposure to diesel exhaust fumes increases hospital admissions and worsens chronic lung diseases. Additionally, diesel exhaust fumes are likely to be carcinogenic (11). Alternative Fuel Vehicles (AFVs) have the potential to minimize health effects and reduce emissions associated with diesel-powered vehicles. Natural gas, an alternative fuel source is “85 to 99 percent methane…clean-burning, cheap, and abundant in many parts of the world,” (4) and can be fueled in the form of Compressed Natural Gas (CNG) and Liquefied Natural Gas (LNG). Natural Gas Vehicles (NGV) “have the potential to emit lower levels of particulate matter, non-methane organic gases, nitrogen oxides, carbon monoxide and air toxins” (14) than their diesel counterparts. The reduction of these emissions has the potential to improve air quality and in-turn public health. To facilitate the adoption of NGVs while maintaining the efficiency of goods movement, the overall objective of this research is to address the existing gap by examining the feasibility of alternative fuel infrastructure and the associated supply chain. To this end, the specific objectives of this study are to estimate the potential CNG demand, to advance the current knowledge on CNG refueling and optimal allocation of facilities that can store CNG and CNG-carrying fleets; identify hotspots and potential sites in the Greater Toronto and Hamilton Area (GTHA) where the allocation of the CNG Fueling Facilities will minimize transportation cost.
Though often overshadowed by passenger transportation in discussions of Connected and Automated Vehicle (CAV) technology, goods movement across North America is evolving rapidly to take advantage of efficiencies facilitated by new technologies and to keep up with changing consumer demand trends. CAV technology is nascent and its eventual impact on the industry is uncertain; many industry players remain skeptical of opportunities for industry reinvention, particularly in the short-term. However, there are early indications of this technology’s safety, employment, financial, and land-use implications. This paper examines the impacts that CAV technology will bring to goods movement operations over the next two decades, particularly in the context of increasing demand for fast, frequent home delivery. Specifically, this paper focuses on truck-based freight with respect to long-distance hauling, and both trucks and automated off-road vehicles (such as drones and sidewalk-based unmanned delivery vehicles) for the purposes of last-mile solutions. Beyond changes to vehicle technologies and operations, changes to distribution centre (DC) scaling, location, and operation are reviewed. Private and government initiatives are explored, as is the use of electricity as a fuel source for freight vehicles. This paper includes primary research gathered through interviews with several large retailers and freight transport/logistics firms to establish baseline use of CAV technology in freight operations, and the likely role of CAV technology over the next decade. Primary research is supplemented by a literature review.
Cost effective and efficient goods delivery in urban areas is an essential business service that contributes directly to the competitiveness of Toronto-based firms. This efficiency is compromised in urban centres like Toronto because of urban congestion, a lack of commercial vehicle parking, inadequate loading facilities, and roadway geometric design. There is also concern with the impacts of delivery vehicles on emissions, the contribution of stopped vehicles on congestion and conflicts between parked delivery vehicles and bicycles that lead to safety concerns. These challenges make the “last mile” of deliveries in urban areas one of the most expensive parts of the logistics chain. Courier companies are particularly concerned with the “last mile” of their deliveries due to their emphasis on punctuality, their high volume of pick-ups and deliveries in the downtown core, and their time constrained supply chain. One of the common distribution systems courier companies operate with is the hub and spoke system. The objective of this project is to evaluate alternative “city logistics” applications in downtown Toronto. The analysis is generalized to be applicable to the operations of any express courier companies serving metropolitan cities. The report is organized as follows. Section 2 describes the scenario of using pack station, Section 3 describes the scenario of mobile hubs, and Section 4 describes the scenario of active transportation modes. Each section also presents the mathematical models used to evaluate each scenario. Conclusions and summaries are presented in the Section 5.
We currently know very little about the willingness and capacity of governance stakeholders to promote, plan and/or advocate for increased e-bike usage in Canadian cities. Planning for the influx of e-bikes in urban centres is challenging given their ambiguity as a motor vehicle and unique performance characteristics, all of which raises uncertainties over how they should be integrated into already crowded systems of transportation infrastructure. Furthermore, the governance and regulation of e-bikes is fragmented and inconsistent. Uncertainties remain over how their uptake may be facilitated or constrained by influential governance stakeholders that possess different understandings of transportation challenges, priority goals, technological and infrastructural preferences, and resource access. In this paper our objectives are to: 1. Analyze the current state of knowledge and perspectives of diverse governance stakeholders towards e-bike technology adoption including related risks and benefits to sustainability; 2. Synthesize key areas of consensus, difference and uncertainty across stakeholders; 3. Identify factors (i.e. physical, political, economic, social) that shape e-bike uptake and impacts in the City of Toronto.
The cost-effective and safe shipping of crude oil is increasingly important as Canada seeks to increase its total oil production and export. The National Energy Board (NEB) suggests that most major pipelines are currently at over 80% utilization (National Energy Board, 2016), while the Canadian Association of Petroleum Producers (CAPP) forecasts a large increase in future production, partly from the Alberta oil sands (Canadian Association of Petroleum Producers, 2016). Meanwhile, recent North American crude oil pipeline projects, such as the Keystone XL pipeline and the Dakota Access pipeline are regularly subject to controversy in the media (e.g., BBC, 2017; The Globe and Mail, 2017; CTV, 2017). Limited pipeline capacities along major routes have already increased dependence on rail as the second-best alternative mode of shipping crude oil (Green and Jackson, 2015). Rail shipments of petroleum bulk crude are expected to increase significantly over the next 10-20 years, especially if globally crude oil prices experience a turnaround and bring new sources into production (e.g., heavier Alberta oil sands) and especially if new pipelines are not built to serve this expected demand. The objective of this research is to develop cost functions for shipping crude oil by pipeline in Canada. These cost functions are a form of “link-performance functions” applied to pipeline route segments for a given mix of shipping conditions. This objective involves several important steps: 1. Researching the relevant explanatory variables that impact the cost of shipping crude oil by pipeline. 2. Collecting data on the relevant explanatory variables (identified in 1.); and 3. Obtaining empirical cost functions based on the Canadian data (collected in 2.)
Since the first high-speed rail (HSR) Shinkansen launched in Japan in 1964, many countries such as China, Japan, France, Germany, Italy and Spain have developed HSR to shorten the commuting time between major cities (Jiang & Zhang, 2016). Nowadays, HSR has become a usual transport mode for tens of thousands of passengers. At first, HSR is only designed for passenger transport. In the 1990s, with the maturity of the high-speed rail network in Europe, railway companies in some countries attempted to express freight transportation by high-speed rail, which yielded remarkable economic and social benefits. The representatives are freight trains launched in France and Germany for delivery of letters, parcels and other light cargoes. In recent years, Euro Carex project using the existing HSR network and high-speed trains to transport cargoes and link major European logistics sites is implementing step by step. Compared to Europe, China owns the world’s largest express delivery market with a rapid growth rate and its high-speed railway network is widespread. Therefore, we believe the HSR freight business will have a better future in China than in European countries. HSR is an efficient and environmental-friendly and energy efficient transportation tool for both passenger and freight traffic. Constrained by the lack of data and materials, we only made general summary and analysis of freight transportation by HSR in this paper. We will continue to pay attention to the development of HSR freight transportation in China and worldwide.
There is a large quantity of research, reports and media articles on the subject of autonomous vehicles. While autonomous vehicles are just one component of the technological developments of artificial intelligence, they are currently generating the most interest. This survey begins with a broad taxonomy of the potential for autonomous vehicles in transportation, and then focuses on the specific issues associated with the advent of fully autonomous cars on public streets and highways.
The fight to enter the taxi market in parts of Canada and elsewhere in the world by Transportation Network Companies (TNCs), like Uber, continues. Uber is still unable to break into the BC taxi market and there will be no new taxis on Vancouver streets until at least Oct. 2017. In Europe, a report published by the OECD in May 2016 states ‘The rapid rise of commercial transport apps such as Uber or Lyft is challenging long-established rules in the for-hire passenger transport market. These platforms often fall outside existing regulations and governments have typically reacted by seeking to block them or by tweaking existing taxi regulation to include such services.’ The report goes on to state ‘Oversight tends to be tilted in favour of established providers - often because of the specificities of street hailing, but also due to market capture.’ This paper continues our description of the regulatory developments in the taxi industry in Toronto and Ottawa. The first section summarizes our earlier paper. The next section presents the taxi industry response to competition. Then the new regulations that were adopted by Toronto and Ottawa are described. The concluding discussion addresses the purpose of such economic regulations.
The transportation planning process has been used intensively by estimating urban travel demand models since the mid-1950s. Researchers and policy makers have been using these models to make informed decisions on the future development and management of urban transportation systems. To date, the majority of the efforts in the literature have been focused on developing aggregate zone based models (TMIP, 2014). In this framework, the urban area is divided into a finite number of zones known as traffic analysis zones (TAZs) that form the units of analysis in the model. However, with the need to capture the travelers’ behavior, there has been a shift towards developing micro-based models that make use of households and individuals as the unit of analysis. In general, the zone based approach has been criticized due to the lack of the realism needed to capture the actual travel behavior observed in the urban area. This paper strives to advance the micro-based paradigm by studying trip generation in the London Census Metropolitan Area (CMA). It does so by developing micro-based trip generation models using a household travel survey that was collected in the year 2009. The focus will be to compare various techniques that could be used to model trip generation (i.e., regression, cross-classification, discrete choice, and count models) at the micro-level.
The Region of Peel is a significant freight hub for Canada and a strategic location for national freight distribution. An estimated $1.8 billion worth of commodities travel to, from and through Peel every day making goods movement a pillar of the regional economy. The Region of Peel is currently undergoing a goods movement planning effort, which includes development of a short term five year strategic plan and development of a long term plan. A component of the long term plan is the identification and assessment of supply chains of importance to the Region by commodity. The rise of online shopping or e-commerce, has forced retailers to seriously reconsider their logistics and distribution of their products. While brick and mortar stores are not disappearing, the need to rapidly deliver goods to both individual homes and stores for pick-up in response to online shopping necessitates a reappraisal of supply chain dynamics. As a result of the changing nature of last mile deliveries, technological changes, and decentralization of distribution, it is important for planning agencies to understand how the commodities of importance move through their jurisdiction. The commodity flow and network analysis completed for the long term plan allows Peel to understand the industries and commodities of greatest importance to the Region in terms of value and tonnage. Commodity attributes are identified using Ministry of Transportation Ontario (MTO) Commercial Vehicle Survey (CVS) data. The CVS data is used to identify the value, tonnage, and origin-destination of commodities. The movement of goods is modelled using the MTO Greater Golden Horseshoe Version 4 (GGHM V4) model in the 2011 base year and the forecast 2031 horizon year. The output of this analysis provides an understanding of how specific commodities move through the Region and the location of current and future congestion. It also provides an understanding of how industries interact with the Regional road network and how their supply chains function. Using this information, Peel can make future investments to benefit goods moving industries, the Region and nation.
During the last decade or so in North America, activity-based travel demand modeling has become more popular than four-step and tour/trip travel demand models among urban and transport modelers. The disaggregation feature of activity-based models can help to capture different aspect of travel behavior complexity, such as flexibility in individual’s activity schedules, and associations between trips and activity participation. To date, researchers and practitioners have employed different approaches for development of activity-based models. A number of important specifications which affect the average level and variability of such models are prediction accuracy, reproducibility, computational time, large scale operation capability, and performance at the household level. The current paper presents cutting-edge methods and progress in developing a new comprehensive pattern recognition modeling framework for use in activity-based travel demand modeling. The framework leverages activity data to derive clusters of homogeneous daily activity patterns, in order to infer the scheduling behavior of individuals. In this paper, numerous new machine learning techniques are employed in the pattern recognition modeling framework. The pattern recognition model is applied to data from the large Halifax STAR household travel diary survey. The proposed modeling framework has much higher reproducibility and shorter computational time compared to other alternative modeling frameworks. Furthermore, the proposed modeling framework can be applied to any applications that contain a group of linked sequences, such as day-to-day variations in transit usage.
Activity-based models aim for a better understanding of people’s desires for, restraints on, and likelihoods to perform activities. They emphasize travelers’ participation in out-of-home activities, because people mostly travel to perform particular activities at different geographic places, rather than traveling just for the sake of travelling. Thus, travel demand is a by-product of the desire or need to participate in different activities. Since activities may take place at different geographic locations, traveling is required. Therefore, analyzing different people’s daily activity patterns in different localities will assist modeling of urban transportation demands. The notion of travel as an induced demand has been well established and accepted by researchers through the work of Oi and Shuldiner (1962), which suggests that one should study travelers’ participation in out-of-home activities first, before studying travel demand per se, as activities generate travel demand (Meyer and Miller, 2001). The set of activities performed by individuals each day is influenced by the choices and preferences of the individual as well as those of their household members, or social groups. Thus, agent-based models such as MATSim aim to obtain the optimal daily schedules so that existing activity travel behavior can be replicated, and future travel patterns can be predicted according to changes in activity timing or location. Construction of satisficing schedules that integrate the resources available for choices are the goal of optimal daily activity schedules. However, these models face challenges such as name and type of activities, sequence of activities, start and duration of activities, composition of the group participating etc. (Axhausen, 2011). Feil et al. (2010) developed a tabu-search based optimizer for the number and sequence of activities and proposed a recycling approach to overcome the computing time problems. Subsequently, Mahdieh (2017) proposed a bi-level optimization model where accuracy of the predicted model has been optimized in the upper level model and utility of participating in each activity has been optimized in the lower level model. Despite all the progress made in activity-based models, there are few studies that emphasize optimization of individual daily activity patterns. Thus, the aim of this study is to design and implement a prototype optimization model that efficiently optimizes a schedule, including the number, type, companionship, location, and sequence of its activities.
Implicit in the decision to take a bus is the choice to accept the possibility of delay or uncertainty in journey duration. Frequent stops and the necessity to navigate urban traffic causes bus transit to be particularly susceptible to delays (Lin et al., 2008). The importance of providing reliable bus service in supporting bus patronage is well accepted in the theoretical literature (Bates et al., 2001). Rider surveys also support the hypothesis that bus reliability is important to patrons (Diab and El-Geneidy, 2012; Eboli and Mazzulla, 2007; Kou et al., 2017). Due to data limitations, empirical revealed preference (RP) analysis connecting observed vehicle reliability and bus mode choice have not been widely attempted. This paper will present detailed Automatic Vehicle Location (AVL) data on New York City (NYC) buses from 2016. Several previous studies have suggested metrics for transforming AVL data into dependability metrics (Bullock et al., 2005; Chen et al., 2009; Diab and El-Geneidy, 2012; Mazloumi et al., 2009; Uno et al., 2009). This study will estimate bus dependability statistics directly from a large AVL data set. Subsequently, the metrics will be taken to mode share data in order to relate bus dependability to local variation in bus ridership. Results suggest service dependability is an important determinant of local bus mode share amongst commuters.