WSP Parsons Brinckerhoff was hired by the Ministry of Transportation (MTO) to develop the Transport and Regional Economic Simulator of Ontario (TRESO), which is a passenger, freight and macroeconomic model covering the Province of Ontario with connections to the rest of the world. When complete, MTO staff will be able to use TRESO to evaluate a wide variety of policy, planning and investment scenarios that will affect major transportation corridors and systems across the entire province, as well as gateways to other North American locations. Commodity and freight modelling is a heavy emphasis within TRESO. Example scenarios of interest to MTO staff include the effects of: economic growth or decline, land-use changes (reflected by adding new firms within a region), changing use of E-commerce, changes to the rail and marine infrastructure and possible trade barriers on truck, rail and marine freight travel patterns. This paper presents an overview of two TRESO components - the commodity flow model and the long-distance truck model. The paper describes their inputs, a brief and high-level description of their structure, and some preliminary model results.
In Canada, Monteiro (2011) estimated that private trucking could account for as much as 85% of urban and 75% of inter-provincial truck movements. Therefore there is a clear need to better understand the role of private trucking in Canada. In particular, this research will inform the redesign of Statistics Canada’s Trucking Commodity Origin Destination (TCOD) survey to include private trucking. Filling this data gap will also improve the information available to Transport Canada and other policy makers that are grappling with an understanding of how to facilitate better freight movement. The paper begins with a selective review of literature on outsourcing in general and on the decision to operate own-account or private trucking specifically. Next, the data used for the analysis are presented along with some descriptive statistics. Then, a multivariate binary choice mode is specified in order to determine those factors statistically associated with the use of private trucking. Results from the model are examined in some detail before the paper concludes by pointing to future research needs.
The objective of this paper is to quantify the energy use and emissions related to the construction and operation of homes and transportation vehicles under a variety of scenarios, in the goal of defining the optimal development type and gaining a better understanding of the differences in sustainability between a few key scenarios. This work will enable urban planning policy recommendations to be made using local data on development types and likely outcomes.
Transit stop plays a very important role in improving transit system performance, maintaining traffic flow, passenger safety and security. In public transportation system, transit stops are the points which effect passenger perception towards transit system and can affect ridership. Till date, operational factors are given more importance; therefore, this paper discusses important passenger perception factors to be considered while planning a transit stop. Guidelines for street transit stop planning are available in several transit agency manuals and mathematical models. However, these guidelines do not report on every street transit factor, vary a lot from each other, and do not give rank-based factor list. Further, transit user opinion is also important to rank the amenities provided at transit stop. To answer these limitations, this paper report on expert and user opinion surveys, their population and sample size, instrument design, preliminary pilot survey findings and procedure for result analysis.
Technological advancements in the manufacturing of key Electric Vehicle (EV) components, especially the battery components, have renewed public’s interest in EV adoption. These advancements continue to improve the competitiveness of EV vehicles in terms of efficiency and ownership cost relative to gasoline-based vehicles. Governments around the globe are supporting policies that encourage the public as well as commercial entities to consider EV adoption on a more substantial scale. As such, governments and the private sector are responsible for the majority of global EV purchases as reported by Sierzchula (2014). Surprisingly, Canada’s global market share of EVs is only 0.4%. (IEA, 2017). This is significantly lower when compared to the European countries such as Norway which leads the globe with a share of 23.3% of global EV market (IEA, 2017). This paper offers new insights on factors influencing the acquisition of EV fleets in Canada. An online survey was designed to collect information from a random sample of 1,008 Canadian businesses and organizations that own and operate fleets. The collected data included organization’s general characteristics, existing fleet characteristics, future acquisition plans and EV fleet prospects. Vehicle fleets were classified into three main types: Cars, Pickup Trucks and Utility Vehicles. The survey tries to identify and understand the factors influencing the preferences and motivations of government and commercial entities as they contemplate adopting EVs in their automobile, pickup truck and utility fleets. The collected data will help identify the circumstances that will lead to higher adoption rates of EVs by these entities.
This paper introduced a transit stop planning tool for evaluating the spacing, location, and design of existing/new transit stop(s) using a transit stop rating index. The rating index accounts for various transit stop planning factors along with their interdependencies and the uncertainty associated with their ranges. As opposed to pure mathematical fundamentals and concepts, the principles of Fuzzy Set Theory (FST) were used. To account for the variability in service planning standards and guidelines among different transit agencies, the developed tool provides transit planners with the flexibility to select relevant factors and change their ranges from a set of recommended default values. For illustration, a random transit stop was evaluated using the developed tool to demonstrate the applicability of tool in practical situations. However, the factor values used for this illustrative example is only assumption based and does not show the real data. Further, the socio-economic and demographic data and ridership data is confidential and is not used in this paper.
Although by implementation of new principles, tools, and techniques infrastructure asset management has been improved and expanded rapidly, however, key concerns resulted by new economic, and sustainability issues, still must be taken into account to develop a comprehensive framework for public transit system management. Existing researches focus more in case of system performance, and level of service while physical indicators are often selected for this purpose and user convenience criteria are ignored. It is also common to see limited budgets with some funds to palliative cosmetic solutions. Many cities face an enormous pressure to handle the ever-growing traffic demand with a limited budget. At the same time, demanding for quality, comfort, and safety by travelers is increasing makes it more complicated to deal with the challenge to convince costumers to abandon the use of private automobile. The asset management of underground transit systems is a complex process as there are different types of facilities (rail cars, stations, tunnels, etc.) with completely different nature including many subcomponents geographically dispersed across a network. This leads to the need to use of a multi-facility multi-criteria assessment and decision making approach when it comes to the management of transit systems. The main objective of this study is to address recent issues should be covered by transit system management frameworks particularly focusing on subway systems to provide a safe, reliable and convenient service in the best interest of any metropolitan transit systems. Research proposes a framework to show how sub-models reflecting new issues could be attached to common transit agency approaches.
The EV market share has increased due to growing concern over environmental issues, financial incentives, and battery technology developments. Currently, EVs have sufficient driving range for intra-city trips where drivers can charge their vehicles at home or at the location of their activities. For inter-city travel, however, the trip distances are much longer and vehicles need to charge en-route to increase their driving range. Charging en-route is different from home-charging. Whereas in home-charging people can leave their vehicles plugged-in and come back later, en-route charging requires that drivers wait until the charging process is complete. Hence, it is critical to have fast chargers that cut down the charging time considerably and increase the charging coverage in order to promote EV penetration in the market. However, the high cost associated with constructing fast charging stations limits the number of charging stations that can be deployed and necessitates choosing their locations optimally. In this study, we solve a nonlinear complementarity problem to optimize the location of fast charging stations to maximize network coverage and minimize total network travel time. Results show that optimizing the location of charging stations can reduce the total travel time by up to 21% whereas unregulated expansion of the charging infrastructure can actually increase the total network travel time.
Increase in daily travel activities coupled with reliance on conventional vehicles, places a significant pressure on the environment through tailpipe emissions. Fortunately, current advancements in battery technology along with the introduction of electric vehicles (EVs) is often considered as one of the more viable solutions to combating climate change and promoting sustainable energy. Due to the scarcity of EVs in the current market, many studies have utilized stated preference (SP) analysis along with estimating different forms of econometric model to identify and to understand factors affecting consumers’ choice decisions regarding new vehicle technologies (for example: Hackbarth & Madlener, 2016; Hoen & Koetse, 2014; Qian & Soopramanien, 2011). In addition, Potoglou and Kanaroglou (2008) provided a comprehensive literature review on various research methods regarding alternative fueled vehicle demand, focusing on SP analysis and different discrete choice models. Moreover, Rezvani, Jansson, & Bodin (2015) focused primarily on empirical studies evaluating various consumer behaviors towards plug-in electric vehicle adoption. While most the existing studies have been concerned with household EV ownership, little has been done to explore the potential of adopting these emerging vehicle technologies by commercial fleets. Public and private organizations typically have high vehicle purchase rates (Dijk, Orsato, & Kemp, 2013) and high average annual mileage (Gnann, Plötz, Funke, & Wietschel, 2015), making them ideal EV adopters; thus, it is important to understand the motivations behind their EV acquisition decisions. Some of these motivations are firm-specific; government agency’s EV adoption is partly driven by restrictive legislations, while the potential profit increase through technological leadership encourages corporations’ EV purchasing decisions (Sierzchula, 2014). The analysis on this study is built on the extensive works regarding new vehicle technology ownership and extends their analyses on consumer rental context. As of 2015, the rental industry accounts for about 69% of all car registrations and approximately 47% of all light truck registrations, which are the highest in both categories (Canadian Automotive Fleet, 2016). The focus of this study is to determine and evaluate the preferences and motivations of Canadian consumers towards renting certain vehicle types using a SP survey.
Transit service reliability is one of the highly valued attributes for users and operators. This paper examines the impact on headway regularity and passenger loads in a simulated bus operation due to the bus capacity limits and the variability of departure headways. For the simulation purpose, four operational scenarios are set up. Same timetable and route characteristics are considered for all scenarios. In the first scenario, it is assumed that buses have infinite capacity and can accommodate all passengers waiting at bus stops. In other scenarios, bus capacity is constrained with different carrying capacities in order to analyze the impact of passengers being left behind when buses are full. The degree of dispatch headway also is varied to examine effects of headway regularity along the downstream of the route. Passengers arrival and dispatch headway of bus are assumed to follow Poisson and Gamma distribution respectively. Travel time between stops is assumed as constant in order to isolate the effect of dispatch headway variability. The simulation studies demonstrate the propagation of headway irregularity along the downstream of routes, a correlation between bus headways and passenger loading that caused bus bunching.
Meeting the needs of passengers will increasingly become a competitive factor for each stakeholder of the air transportation system. To achieve this goal, a passenger-centric view needs to be employed. Since passengers may encounter many modes of transportation during travel, adopting the passenger-centric view leads in a natural way to intermodality. Timely provision of relevant information is one of the most demanded services. Exact knowledge of information and respective interfaces required may lower the inhibition threshold of stakeholders to share information. In this paper, we describe the data model and the interface model necessary to implement a Passenger Information System on a mobile device. The features of our system comprise real-time and personalized information about departure times of urban transport and air transport, estimated time to complete the next step, such as check-in or security check, and estimated spare time for visiting the retail area. Data model and interface model are described by using standard methods in computer science, so that the description is independent of the implementation itself. Furthermore, it is transferable to other situations where a Passenger Information System is to be installed. In future work, we will implement a Passenger Information System on the basis of the data interface model described. We will evaluate the performance of the data interface model described with respect to reliability and throughput. We will answer the question of whether the model is appropriate for real-world applications. A further step would be the dissemination of the — hopefully positive — results of the evaluation among stakeholders in order to propose an information system which is independent of the data’s owner.
Our literature review reveals that most applications of Computable General Equilibrium (CGE) models in transportation fall into comparative analysis, where scenarios before and after transportation projects or policies are compared. However, the potentials of these models go beyond just comparative analysis. In this paper, a framework is proposed that introduces the concept of optimization into the CGE context. This framework is particularly aimed at optimizing transportation infrastructure investment over space and time. The proposed framework offers an empirically-based conversion of infrastructure investment (i.e., in $) into transportation network attributes (e.g., capacity). The proposed methodology is described in the next section, followed by a formulation of a simple non-trivial model. Then, concluding remarks and current and future research directions are presented.
Productivity is a measure of how a firm, such as a railway company, uses inputs like labour, material, capital, etc., to produce outputs such as transportation movements and services. An increase in productivity occurs if the company produces a greater quantity of its outputs with the same quantity of inputs, or uses less quantity of inputs to produce the same volume of outputs. Productivity can be compared between firms, industry sectors, regions, countries, or even groups of countries in economic or political blocks. The aim of this paper is to present a new method to calculate an index related to the Canadian Railways Total Factor Productivity.
This paper presented cluster-based analysis for the home-based tours. The cluster-based approach can be utilized to capture differences between homogenous clusters of travelers in terms of mode choices and their influential factors. The immediate future work includes predicting modal shares for comparing the accuracy of the model. Since this study used public-use micro data, one limitation of this study is that it cannot incorporate the effects of built-environment variables into the models. The next step will include incorporating the built-environment variables into the tour-mode choice modeling using the Halifax Space-Time Activity Research (STAR) dataset. From the cluster analysis, it has been found that there exist significant differences among the four travelers’ clusters in terms of tour attributes. This explains the need for incorporating the heterogeneity of travelers’ grouping in the travel demand forecasting model. An improvement of this work can be achieved by modeling the household interactions for joint tours, with special attention to chauffeuring trips. This study offers a clustering approach to model the travelers in separate internally homogenous clusters, so that mode-choice modeling can reflect the latent heterogeneity among the travelers.
The physical movement of goods plays a key role in many market transactions, making the transportation system an essential foundation for a national economy. As a trade-reliant nation with its population spread over a vast landscape, Canada is particularly dependent on an effective transportation system. And, in order to assess the national transport system and its ability to move freight, quality statistical information is required. In several OECD countries, a Freight Analysis Framework is used as a planning tool for assessing the transport network and its capacity to meet projected demand. This study investigates the potential of capturing import shipments by examining American outbound shipments to Canada. It begins by reviewing a plausible Canadian framework and data considerations. To further our understanding of this shipper-based approach, it examines the number, value and weight of American export shipments to Canada from the 2012 CFS. Next, it focusses on other characteristics such as the region of origin, type of industry, commodity classification, and mode of transport. The study concludes by discussing the technical and organization considerations in moving forward.
Canada signed the Canada-European Union (EU) Comprehensive Economic and Trade Agreement (CETA) in October of 2016, which was later ratified by the European Parliament in February, 2017. This agreement eliminates tariff barriers and enhances accessibility to the EU market (Government of Canada, 2016). The Canadian government investigates the future impacts of CETA on the nation’s economy, regulation, society, and employment size, but evaluation of Canada’s transportation system under the CETA is almost forgotten (Bachmann, 2017). The objective of this paper is to assess the potential impact of CETA on the Canadian transportation network by estimating the origin-destination trade flows, mode share, and transportation flows before and after the CETA.
Regional Logistics Performance (RLP) has been lacking in the literature. There are three fundamental reasons for this: First, even Logistics Performance Index (LPI) at the country level is relatively recent. Second, competitiveness at the regional level, compared to the national one, has not yet been well recognized. Third, it is hard to create a consensus on globally-accepted metrics to be used. Yet, RLP may be a vital tool for promoting regional prosperity and its benchmarking with its domestic counterparts. RLP is challenging, especially boiling it down to an index score, for two reasons: First, who will do it, systematically? Second, is the region industry-dense enough to collect data from? With these in mind, Table 1 proposes a framework for RLP that is relatively-easy to measure and use. The metrics are inspired by LPI measures and consider possible regional specificities.
The Complete Streets Design and Construction Standards (CSDCS) document provides a single point of reference that supports the planning, design, and construction of Complete Streets in Edmonton. It integrates the best practices in Complete Streets design philosophy and guidance introduced in the City’s 2013 Complete Streets Guidelines with the City of Edmonton’s former Roadway Design Standards and Construction Specifications. The intent of these Complete Streets Design & Construction Standards is to encourage a holistic approach to street design that will develop a network of streets that is safe, attractive, comfortable, and welcoming to all users in all seasons, while considering operational and maintenance challenges. The document introduces the ‘Design Domain’ approach which allows flexibility in design through variance in street element design values based on the modal priorities and context of a specific corridor. From a technical perspective, the document is intended primarily for engineers, planners, and the development industry. Though the document can be utilized by communities and the public, a less technical primer companion document will better serve those users. The Complete Streets Design and Construction Standards are intended to be a living document, with regular updates to incorporate changes in best practice and their application in the Edmonton context.
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.
Parking space near shops, restaurants, and other destinations is often scarce or expensive in dense urban areas. Urban shopping malls may provide little or no parking space, or charge high hourly rates. Shoppers end up searching for cheaper parking on the street, or walking to and from residential neighborhoods. Spillover parking can also be a problem near train and metro stations, sports stadiums, concert halls, public parks, and other places that attract lots of people. Cities and towns often tackle spillover parking by enacting residential parking permit programs that ban nonresidents from parking in residential areas. Permit systems can be effective at eliminating parking problems for residents. However, like other non-price rationing tools, permit systems are generally inefficient because they allow residents to park free or for a nominal fee, and thus do not assure that parking space is allocated to those who value it the most highly.2 Residential parking permits are also hard to enforce (Rye and Llewellyn, 2016). Yet, despite these weaknesses, local politicians prefer residential parking permits to priced-based policies because residents benefit from permits, and can express their preferences at the voting booth. Another common policy to curb spillover parking is to impose minimum parking requirements, differentiated according to type of land use. Yet minimum parking requirements have well-studied drawbacks. Parking space is often underutilized outside regular business hours, and more generally at times of low demand. Expanding parking capacity is also unsightly, and it encourages people to travel by car (Guo, 2013ab). As Shoup (2013) argues at length, spillover parking can be addressed by better parking pricing. Priced-based policies enhance efficiency because they are demand (and thus value) driven. Their goal is not necessarily to eliminate spillover parking, but rather to maximize the combined welfare of residents, nonresidents and businesses. In this paper, we analyze three policies to deal with spillover parking generated by a shopping mall located in a residential area: (1) a curbside parking fee, (2) regulation of the mall parking lot fee, and (3) regulation of mall parking lot capacity. If the regulated lot capacity exceeds the mall’s profit-maximizing capacity, the third policy amounts to a minimum parking requirement, but since demand is static in the model this does not result in underutilization of capacity.