Chris Bachmann

Fusing a Bluetooth Traffic Monitoring System With Loop Detector Data for Improved Freeway Traffic Speed Estimation

Anonymous probe vehicle monitoring systems are being developed to measure travel times on highways and arterials based on wireless signals available from technologies such as Bluetooth. Probe vehicle data can provide accurate measurements of current traffic speeds and travel times due to their excellent spatial coverage. However, presently probe vehicles are only a small portion of the vehicles that make up all of the traffic in the network. Alternatively, data from conventional loop detectors cover almost all the vehicles that have traveled along a road section, resulting in excellent temporal coverage. Unfortunately, loop detector measurements can be imprecise; their spatial sampling depends on the loop detector spacing, and they typically only represent traffic speed at the location of the detector and not over the entire road segment. With this complementarity in mind, this article explores several data fusion techniques for fusing data from these sources together. All methods are implemented and compared in terms of their ability to fuse data from loop detectors and probe vehicles to accurately estimate freeway traffic speeds. Data from a Bluetooth traffic monitoring system are fused with corresponding loop detector data and compared against GPS collected probe vehicle data on a stretch of Highway 401 in Toronto, Canada. The analysis shows that through data fusion, even a few probe vehicle measurements from a Bluetooth traffic monitoring system can improve the accuracy of traffic speed estimates traditionally obtained from loop detectors.

Developing A Multi-Scale Multi-Region Input-Output Model

Many efforts have recently been devoted to developing global multi-region input–output (GMRIO) models. Unfortunately, the scales of GMRIO models do not allow them to capture the heterogeneity of regions within a single country. Multi-scale models can provide more comprehensive analyses capable of capturing the interdependencies of the global economy while preserving regional differences. The primary objective of this research is to develop methods for integrating multi-region input–output data sets from multiple spatial scales into multi-scale multi-region input–output (MSMRIO) models. These methods result in models that may have unusual features such as non-square trade coefficient matrices and a mix of industry-by-industry and commodity-by-commodity technical coefficients. To demonstrate the feasibility of MSMRIO modelling, a Canada-centric model was developed. This model includes 47 countries and Canadas 13 subnational regions. A MSMRIO model provides a tool to analyse global issues with a more spatially detailed focus.

Applications of Random-Utility-based Multi-region Input–Output Models of Transport and the Spatial Economy

Abstract An introduction to random-utility-based multiregional input–output models used for the purpose of spatial economic and transport interaction modelling is provided. The main methodological developments and important results of a dozen applications from the years 1996–2013 are described. This is followed by an outlook of potential future directions. Further research is mainly needed in five areas: (a) overall validation of the method, perhaps through back-casting applications on infrastructure plans with observed trade impacts; (b) extensions of trade coefficient models to add realism and improve accuracy; (c) the use of multi-scale modelling to capture interdependencies between geographical scales and to improve the representation of exports and imports; (d) improvements in the representation of price effects, as well as innovation and technological progress, by way of variable technical coefficients; and (e) a deeper investigation of the algorithm used to include elastic selling prices.

Multisensor Data Integration and Fusion in Traffic Operations and Management

Widespread technological development and deployment have created an abundance of data sources for traffic monitoring. A database that integrates data from all these technologies would maximize coverage of the network, given the available data. Sometimes, however, there are multiple independent measurements of the current traffic conditions for a particular portion of the network. In these cases, a variety of data fusion techniques can be used to achieve better estimates while helping to overcome information overload. This paper discusses several techniques for fusing data from competitive sensor configurations, describes the analytical foundation of these techniques, and interprets how each technique might be used most appropriately. In addition, these data fusion techniques are implemented and compared relative to their ability to accurately and reliably estimate traffic speeds. A real-world case study in Toronto, Ontario, Canada, demonstrates that estimates from data fusion techniques that pull loop detector data and probe vehicle data from an integrated database are more accurate and reliable than estimates based on individual data sources. Consequently, these data fusion–based estimates can be taken with greater certainty and confidence.

Life-Cycle Assessment of Diesel-Electric Hybrid and Conventional Diesel Trucks for Deliveries

Purolator is a Canadian courier company that recently introduced hybrid electric vehicles (HEVs) into their fleets. This paper estimates the fuel savings and greenhouse gas (GHG) reductions for Purolator’s hybrid trucks using a life-cycle assessment executed with GHGenius, a Government of Canada model. Overall, it was found that Purolator’s hybrid diesel trucks reduce GHG emissions by 23% and 8% for city and highway driving, respectively. The results confirm that switching to an HEV fleet could reduce CO₂ emissions during vehicle operations by 25%, as the vehicle manufacturer (Azure Dynamics) has advertised. Moreover, the modeled emission reductions (1,668 t CO₂-eq) and fuel savings (609,000 L) over a distance of 5,200,000 km are similar to Purolator’s advertised values (1,900 t CO₂-eq, 645,000 L). The life-cycle costs of hybrid delivery trucks do not presently make them financially favorable alternatives to conventional diesel trucks, but their financial competitiveness is sensitive to vehicle service life, diesel fuel price, assumed discount rate, and incremental hybrid cost.

Global Trade Creation, Trade Diversion, and Economic Impacts from Changing Global Transport Costs

Despite the complex political and economic trade environment, the generalized costs of transportation still play an important role in determining trade patterns. This paper analyzes how global trade patterns would simultaneously change as a result of new global transportation costs and for which countries the new patterns of global trade would be beneficial or detrimental. A random utility-based multiregion input–output model helps quantify the size of the impacts, identify the countries and sectors that would gain or lose the most, and explain the change in trade patterns that would create these impacts. Results suggest that Canada is most susceptible to negative economic impacts caused by decreases in global transportation costs, mainly because of its important trade relationship with the United States. Canada’s economy benefits from its proximity to the United States, but as transportation costs decrease, this proximity becomes less relevant as the United States increasingly trades with more distant countries. The United States suffers the same susceptibility to negative economic impacts with decreases in global transportation costs, but to a larger absolute (smaller relative) extent. However, global transportation cost reductions are especially beneficial to Japan, China, and South Korea, which absorb much of the trade diverted away from the United States and Canada.

Modeling the Impacts of Free Trade Agreements on Domestic Transportation Gateways, Corridors, and Ports

Canada has recently made progress with several free trade agreements (FTAs), and although the government has carried out considerable analysis of their potential impact on the Canadian economy, little to no work has been done to assess the potential impact on Canadas transportation system. The objective of the research was to estimate the impacts of recent and forthcoming FTAs on Canadas domestic trade infrastructure. This study extended a typical computable general equilibrium simulation of an FTA by estimating high-level domestic supply chain characteristics (i.e., subnational region of origin or destination, sub-national region of exit or entry, international transportation mode, port of clearance) and by converting the resulting trade flows to freight flows measured in tonnage. The results indicate that the Comprehensive Economic and Trade Agreement (CETA) between Canada and the European Union (EU) may have had large impacts on Canadas Continental and Atlantic Gateways, especially at the Port of Montreal, Quebec, as a result of trade creation with the EU. CETA also has had impacts on various crossings at the U.S. border as a result of trade diversion with the United States. Simulations, however, suggested that the Canada–Korea Free Trade Agreement has had relatively small impacts, mostly concentrated in the Asia-Pacific Gateway, particularly at the Port of Vancouver, British Columbia. Although the impacts were FTA-specific, this research demonstrated the need to consider FTAs in commodity forecasting and freight transportation planning, because they could make sizable changes to future freight flows on domestic transportation infrastructure.

Developing an Empirical Pipeline and Rail Crude Oil Mode Split and Route Assignment Model

In recent years, improvements in pipeline capacities and connectivity have been inhibited by environmental and political concerns (e.g., the Dakota and Keystone XL pipeline expansion projects). This has resulted in a greater dependency on rail transport, and this modal shift of crude oil from pipeline to rail is likely to become more pronounced in the future. Therefore, there is a need to assess the impacts of future changes in pipeline/rail network connectivity, modal attributes, and shipment protocols, on the expected pattern of crude oil shipments. This paper presents a rule-based mode split and route assignment model that reflects real-world allocation, assignment, and apportionment rules. The decision-making process underlying this model is the shipper, who is prioritized by the carrier when there is limited pipeline capacity available. The proposed algorithm allows for the inclusion of crude oil shipments and the pipeline network into conventional freight demand models by capturing the complex interactions of crude shippers, pipeline carriers, and regulatory bodies. This paper demonstrates how the model can be used to predict changes in Canadian crude oil flow patterns and mode shares subject to changes in specific transportation network attributes or crude oil demands.

Assessment of Canada’s Transportation System under the Comprehensive Economic and Trade Agreement

Canada signed the Canada–European Union (EU) Comprehensive Economic and Trade Agreement (CETA) in October 2016, which was later ratified by the European Parliament in February 2017. This agreement enables Canadian establishments to trade openly with EU members. Despite the Canadian government’s economical analysis, no study has been undertaken to investigate the impact of CETA on Canada’s transportation network. The objective of this paper is to assess the potential impact of CETA on the Canadian transportation network by estimating origin–destination trade flows, mode shares, and transportation flows before and after the agreement. Annual provincial commodity flows are obtained from a computable general equilibrium model. Mode shares are determined based on US commodity flow survey data. Finally, changes in freight flows by the rail and truck modes as a result of CETA are examined. The model results indicate an increase in freight movement to and from Eastern Canada and a decrease in trade movement near the US–Canada borders and Western Canada.

Designing computable general equilibrium models for transportation applications

ABSTRACT This paper presents a review of Computable General Equilibrium (CGE) model applications for spatial economic and transport interaction modelling. This paper has three objectives (1) To deliver an up to date and comprehensive literature review on applications of CGE models in transportation, (2) To analyze the different methodological approaches and their theoretical and practical advantages and disadvantages, and (3) To ultimately provide guidance on designing CGE models for various transportation analyses. The content of the paper is as follows: first, a brief introduction to CGE models is provided. The history of CGE models is traced, ranging from their origins and seminal applications in economics, to their eventual adoption in transportation research. This is followed by a comprehensive review of the application of CGE models to transport projects and policies. Various applications in transportation are reviewed in terms of their intended application, as well as their treatment of space and time. Finally, these applications are contrasted with respect to their methodological approaches, with a close examination of various influential model choices. Here, the essential design choices made within these model applications are explained and debated, to clearly elaborate on the workings of the models and the design choices facing CGE model developers.