Civil and Environmental Engineering

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This is the collection for the University of Waterloo's Department of Civil and Environmental Engineering.

Research outputs are organized by type (eg. Master Thesis, Article, Conference Paper).

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Now showing 1 - 16 of 16

A Route-Choice Model for Predicting Pedestrian Behaviour and Violations
(University of Waterloo, 2024-08-19) Lehmann Skelton, Christopher; Bachmann, Chris; Hellinga, Bruce
Pedestrians exhibit diverse behaviours, including crossing violations. Traditionally, development of behavioural models has been divided into route choice and crossing behaviour. Route choice models are stochastic and focused on crowd dynamics, while crossing behaviour models are probabilistic or deterministic and focused on local-level behaviours. Route choice and crossing behaviour are often addressed separately, but they are inherently related. This research proposes a new pedestrian simulation model where pedestrians navigate through an intersection or mid-block environment, modelled as a grid. Each cell is assigned a cost that varies over time based on the presence of nearby vehicle traffic or changes to signal indications. Each pedestrian perceives the costs in the environment uniquely depending on their own personal preferences, like desired crossing gap or comfort committing a violation and seeks to minimize their total path cost. Pedestrians who are more comfortable committing violations perceive a lower cost for committing a violation. This approach integrates crossing behaviour with route choice and models the trade-offs of engaging in a particular behaviour. The proposed model is calibrated using video data. The model was applied to three case-studies: a stop-controlled intersection, mid-block crossing, and two crosswalks along the minor approach of a signalized intersection. The model simulates the trade-offs between walking on different surfaces, as well as the trade-off between waiting for a gap in traffic to cross, versus diverting to the nearest designated crosswalk. In the third case study, the model successfully reproduced the proportion of pedestrians crossing against the signal for the north leg crosswalk but did not reproduce the proportion of violations for the south leg crosswalk, which is across a private access. Further investigation should be undertaken into the causes of this and other differences.
ANALYSING ATMOSPHERIC IMPACTS OF REGIONAL TRUCK EMISSIONS USING AN INTEGRATED MODELLING APPROACH
(University of Waterloo, 2018-10-19) Wang, Wilson; Bachmann, Chris; Saari, Rebecca
Transportation technology is providing new ways to mitigate multipollutant emissions co-emitted from on-road sources. Zero-emission vehicles (ZEV) are more common in passenger vehicles and other light-duty vehicles; however, they remain a relatively new technology for most medium-duty and heavy-duty vehicles. As more trucks are adopting zero-emission technology, we need to evaluate whether these mitigation strategies are sufficient in meeting regional reduction goals. Previous studies have evaluated the multipollutant impacts of trucks and other vehicles; however, these methods estimate vehicle activity by empirical data such as surveys, which, unlike process-based models, are not amenable to evaluating significant future technology adoption. This research presents a new method to quantify the atmospheric impacts and evaluate mitigation strategies of zero-emission technology in trucks at a regional scale using an integrated assessment model (IAM). This model establishes a connection between EMME, a travel demand model, MOVES, a mobile emissions simulator, and EASIUR, a regression model that produces marginal damage estimates. The IAM estimates a baseline and compares the total damages of alternative scenarios, using different ZEV adoption rates applied to trucks. The annual, ground-level emissions were estimated for the following pollutants using the developed IAM: primary PM2.5, NOX, SO2, NH3, CO2, CH4, and N2O. The results from the application of the IAM to the baseline scenario show that the total annual damages resulting from atmospheric emissions from trucks for the Province of Ontario in 2012 is approximately $1.82 Billion (2005 USD). Most of these damages are in Southern Ontario, with Toronto, Peel and York being the top three contributors. Adoption of ZEV decreases these damages linearly. Ontario has an adoption rate goal for ZEV of 5% by 2020. This rate is assumed to hold true for trucks in this transportation network. This goal would yield approximately $89 Million (2005 USD) in benefits annually from trucks alone. This result varies by up to ±25% according to the sensitivity analysis related to the travel and emissions models. Future work should focus on the relationship between emissions to damages, which likely remain the largest source of uncertainty.
Analysis of Risks and Cost Overruns in Design-Bid-Build Highway Infrastructure Projects in Ontario
(University of Waterloo, 2017-09-27) Chahrour, Lara; Haas, Carl; Bachmann, Chris
Cost overruns commonly occur in infrastructure projects, and when the owner is a government entity, these overruns may disrupt the funding available for other projects. Research on large projects indicates that actual project costs are on average 20% higher than estimates for road projects and 34% higher than estimates for tunnel and bridge projects. Other studies that reiterate the presence of cost overruns report values between 3.9 and 10 percent. Risk management can be used to identify and assess risks that may cause overruns and develop risk response plans to address them. The objective of this research is to use risk management knowledge to identify and assess project risks and their expected impacts on highway infrastructure projects in Ontario. The studied Ministry of Transportation of Ontario (MTO) projects have an average cost overrun of 5.2% of tender value for new construction projects, and 11.5% for rehabilitation projects. The risk identification and analysis is followed by a comparison between MTO’s risk management experience and other typical North American organizations that are involved in transportation infrastructure such as Infrastructure Ontario and the California Department of Transportation, as well as other contract delivery methods such as design-build and public- private partnerships. From analyzing 986 risk events, this research identifies design scope changes, material, and latent conditions as the main risks that appear to influence cost overruns for rehabilitation projects. For new construction, the main risks are design scope changes, latent conditions, and permits and regulations. Once the risks are identified and analyzed, action is required to manage the risks that are considered most important. This thesis touches lightly on possible risk management actions for the identified risks.
Analyzing the Competitiveness of Transit-Integrated Ridesourcing Systems
(University of Waterloo, 2022-12-20) Terry, Jacob Ross; Bachmann, Chris
Ridesourcing platforms operated by transportation network companies are becoming increasingly popular. Municipal transit agencies have rapidly launched integrated systems with ridesourcing vehicles to extend the reach of their fixed-route transit networks and as a response to changes in the transportation system. These integrated systems have not been critically evaluated, and agencies are implementing ridesourcing systems without much precedence or guidance concerning the integration of transit and ridesourcing. Past research on demand-responsive transport assumed the majority of trips were booked a day or more in advance using subscriptions. This research considers how ridership may change due to the immediacy and convenience of app-based booking for on-demand transit. The objective of this research is to determine the spatial characteristics of transit-integrated ridesourcing networks that best support and encourage use of the greater transit network. A series of spatial attributes were identified based on literature and existing systems, which formed the basis of the research. A recent transit-integrated ridesourcing pilot in Waterloo, Ontario was evaluated for competitiveness with other alternatives and to observe changes in spatial and temporal characteristics. Through this evaluation, a trip typology was developed that other transit agencies can use to evaluate the spatial competitiveness of their transit-integrated ridesourcing systems. The findings of the evaluation indicate that the trips taken in the pilot were mostly complementary to transit, and that the pilot was both growing in weekly ridership and trending towards trips that do not compete with fixed-route transit. A revealed-preference/stated-preference survey was conducted in the same geographical area as the former pilot to determine the combinations of spatial attributes that would best entice residents. 230 responses were gathered from the survey. Qualitative questions from the survey revealed that COVID-19 was not perceived as a deterrent for fixed-route transit or transit-integrated ridesourcing, that car ownership and bicycle ownership correlated with the respective likelihood of driving or cycling, and that fare card or pass ownership did not correlate with the likelihood of taking transit. The lack of familiarity among respondents with the pilot that had previously operated in the area indicates that poor advertising of the service may have been a contributor to ridership not meeting agency targets. A non-linear Bayesian mixed logit model was estimated using the stated-preference portion of the survey, using 2990 best and worst observations (13 scenarios from each of the 230 respondents). The model was applied to a series of representative trips through scenario analysis to determine how mode share would change under various combinations of spatial and operational characteristics. Respondents were found to have similar perceptions of transit-integrated ridesourcing and fixed-route transit. For time attributes (e.g., total, wait, walk), respondents showed the highest sensitivity in the 5-10 minute range. Adjusting the demand patterns of the transit-integrated ridesourcing service to be more permissive of different origin-destination pairs considerably increased the expected mode share for transit-integrated ridesourcing, but may require caution due to the negative impacts in some scenarios for fixed-route transit. The largest shifts in mode share came from directly charging for parking, where the mode share for auto dropped from over 90% to under 50% in most cases.
Assessing Input Uncertainty in Commodity-Based Freight Demand Models
(University of Waterloo, 2021-09-27) Aguas, Oriana; Bachmann, Chris
Freight demand models are a set of tools utilized for the forecasting, planning, analysis, and/or optimization of the movement of commodities, such as the billions-of-dollars-worth of goods and services that are moved annually in Canada, and they contain uncertainty. There are two types of uncertainty that affect freight demand models: input and model uncertainty. Input uncertainty is concerned with the fact that there is error in the data used as inputs to model transportation demand such as biased surveys, incomplete datasets, varying commodity and industry classifications, etc. Model uncertainty is concerned with the fact that the model specification and calibration/estimation may contain error such as omitted variables, inappropriate assumptions, simplifications, etc. There is a lack of understanding surrounding the uncertainty of freight demand models. Regardless, these models are widely researched, developed, and applied without characterizing the uncertainty of typical data sources used as inputs. The contributions of the variation present in different inputs to the model results are unknown, making it impossible to know the robustness of the model outputs or how the results might be improved. The literature review revealed that the most common freight model classification system is based on the unit of demand generation, the most used freight demand models in the North American practice are commodity-based, and input uncertainty has a greater effect on transportation demand models. Thus, this thesis proposes a formal five-step framework (i.e., uncertainty source identification, distribution of source identification, simulation, estimation of output distributions, and analysis of results) to analyze the effects and propagation of input uncertainty on the uncertainty of the outputs in commodity-based freight demand models. The framework is applied to an Aggregate-Disaggregate-Aggregate version of a strictly empirical commodity-based freight demand model used to analyze the effects the Comprehensive and Progressive Trans-Pacific Partnership on Canada’s trade infrastructure. Essentially, uncertainty for three inputs is introduced and a set of outputs is simulated through repeated simulation. The three inputs are high level supply chain characteristics, value-weight ratios, and domestic mode shares – each being an input to one sub-model of the freight demand model. Dispersion, confidence intervals, and performance against the outputs of an illustrative base case are explored. In general, the case study model generates consistent results to the base case when looking at the conclusions of aggregated outputs, despite the tendency to high variance of the disaggregated outputs and the poor results of the confidence interval analyses. Implementation of the framework generated insight on the accuracy of the case study model, and it highlighted the specific instances where the modeler needs to be more cautious of the results when using only point data, as in the illustrative base case.
Comparative Assessment on Static O-D Synthesis
(University of Waterloo, 2022-08-02) Lin, Tina; Bachmann, Chris
Recognizing the benefits of data and the information it provides to travel demand is pertinent to network planning and design. Technological advances have led the ability to produce large quantities and types of data and as a result, many origin-destination (O-D) estimation techniques have been developed to accommodate this data. In contrast to the abundant choices on data types, data quantity and estimation procedures, there lacks a common framework to assess these methods. Without consistency in a baseline foundation, the performances of the methodologies can vary greatly based on each individual assumption. This research addresses the need for techniques to be tested on a common framework by establishing a baseline condition for static O-D estimation through a synthetic Vissim model of the Sioux Falls network as a case study area. The model is used to generate a master dataset, representing the ground-truth, and a subset of the master dataset, emulating the data collected from real world technologies. The subset of data is used as the input for the O-D estimation techniques where the input is varied to evaluate the effects of different levels of coverage/penetration of each data type on estimation results. A total of five estimation techniques developed by Cascetta and Postorino (2001), Castillo et al. (2008b), Parry and Hazelton (2012), Feng et al. (2015) and X. Yang et al. (2017) are tested with three data types (link counts, partial traces, and full traces) and two traffic assignment conditions (all-or-nothing and user equilibrium). The result of this research highlights the uniqueness of each network situation and highlights the outcomes of each approach. The wealth of data does not directly equal better information for every methodology. The insights that each data type provides each estimation technique reveals different results. The findings of this research demonstrate and supports that an established testbed framework supports and enhances future O-D estimation scenarios as it pertains to general O-D estimation and extensions of existing techniques.
DEMAND MODEL FOR CRUDE OIL RAIL AND PIPELINE SHIPMENTS IN CANADA
(University of Waterloo, 2018-01-24) Morrison, Adam Peter Lloyd; Bachmann, Chris; Saccomanno, Frank
Global energy demand is expected to increase significantly over the next 10-20 years, and in the absence of an increase in pipeline capacity, Canadian crude oil shipments are likely to be diverted to rail. The current rail loading capacity originating in western Canada is 754,000 barrels/day. This rail capacity is expected to meet the 1 million b/d deficit between western Canadian production forecasted for 2025 (5 million b/d) and existing pipeline capacity (4 million b/d). Western Canadian production is further forecast to reach 5.4 million b/d by 2030, representing a 39% increase from 2016. The primary objective of this research is to obtain accurate current estimates of crude oil shipments by pipeline and rail in Canada (tonnages and volumes), and to use these data to calibrate and evaluate an empirical model of crude oil shipments by shippers’ mode choice (pipeline and rail) and route selection. Modelling crude oil shipments allows for an assessment of the impacts of future changes in pipeline/rail network connectivity, modal attributes, and shipment protocols, on the expected pattern of crude oil shipments. Origin Destination (OD) demands were based on empirical trade data. In particular, crude oil shipments beginning and ending in Canada are available from CANSIM, Statistics Canada’s key socioeconomic database. These data were supplemented with other data from the National Energy Board (NEB) and the U.S. Energy Information Administration. Due to data limitations and the need for more disaggregated zones to characterize crude oil shipment patterns more precisely, shipments originating from (destined for) British Columbia or Alberta were split based on total production (attraction) data from the Canadian Association of Petroleum Producers, Alberta Energy, and the Canadian Fuels Association. Numerous data sources were compiled to estimate cost functions for shipping crude by pipeline and rail in Canada. For pipelines, cost performance functions (shipper tolls) were found to depend significantly on shipping distance, route pipe diameters and shipment destination (domestic vs international). Moreover, medium and heavy crude were found to be more expensive to ship compared to condensate and light crude due to their lower viscosities. For rail, the distance shipped, a terminal fee and an international tariff surcharge were found to be statistically significant in explaining shipper costs along a route. These pipeline and rail cost models yielded R2 values of 0.85 and 0.83, respectively. Conventional Random Utility Models (RUM) fail to capture the complex interactions of pipeline shipments to determine mode and route choice shipper decisions, resulting in the development of a rule-based approach for mode choice and route assignment. This research found that the mode split, and route assignment of crude oil shipments are jointly determined by shipper types, destination types, prioritization rules, and allocation rules. This approach was validated by comparing predicted throughputs with those reported for Canada’s Group 1 and Group 2 export pipelines, and applied to determine the impact of a 39% increase in western Canadian crude oil production, as forecasted by CAPP for 2030. Assuming no new pipeline infrastructure is constructed before 2030, several rail lines would carry increased amounts of crude oil.
Determinants of Rapid Transit Planning Processes in Ecuador
(University of Waterloo, 2021-06-21) Arias Arellano, Juan F.; Bachmann, Chris
This research presents a novel framework for the quantitative and qualitative analysis of rapid transit planning processes based on a mixed-method approach, along with the corresponding results: the relative importance of factors that affect the projects, a ranking of the critical barriers that hinder their implementations, and the implications of private participation. The planning and implementation of rapid transit in Ecuador has proven to be a daunting endeavor. Nineteen projects have been planned, but only nine have reached implementation. This research seeks to understands how these projects’ design and implementation processes were shaped. The analysis of the design process focuses on how different factors (demand, local conditions, financial, social and political) have influenced all of the rapid transit projects in Ecuador over the past three decades by evaluating their relative significance on each system component (alignment, size, and technology). This analysis uses a multiple-case methodology including in-depth interviews with the senior members of the technical teams, as well as a survey component based on the Analytic Hierarchy Process (AHP) for quantification of the relative significance of the factors. The analysis shows five key results: 1) Each project was unique and external factors introduced a varying degree of complexity into each planning process; 2) The systems’ alignments and sizes were mostly driven by demand and local conditions (i.e., a rational planning process); 3) The main factor driving technology selection has evolved over time from system demand to political (i.e., a political bargaining approach); 4) Negative economic conditions had a large influence on the factors of all project components; and 5) There is a lack of rational alternative evaluation and an absence of corresponding tools/guidelines in Ecuador. Nonetheless, several processes included practices that contributed to a more rational planning process: lifecycle cost analysis for the various technology alternatives, explicit decision-maker guidelines, transferring the demand risk to the private sector, and the use of Multicriteria Decision Analysis (MCDA). In terms of project implementation, this research evaluates the critical barriers for project implementation along with corresponding mitigation measures. A mixed-method, based on an existing theoretical framework and Best-Worst Scaling (BWS), is proposed and applied. The analysis is grounded by in-depth interviews and surveys conducted with the technical teams of the planned projects. Results show the top tier of the critical barriers is composed of 1) Lack of political leadership, commitment or continuity; 2) Underestimation of implementation complexities; 3) Political frictions; and 4) Rushed planning processes. Seven key mitigation measures were identified and linked to each barrier: 1) Connect the social, political and technical perspectives; 2) Aim at starting construction before the end of political cycles; 3) Increase the private role in the procurement strategy; 4) Increase community input during planning; 5) Generate public opinion monitoring; 6) Adapt projects features to community input (when applicable); and 7) Focus on the implementation of one trunkline at a time. Moreover, evidence suggests critical barriers are not technology specific, but rather endemic to the planning process and that the projects’ critical political dependence promoted planning based on political cycles in detriment of long-term efforts. These findings align with previous results from developing cities, confirming the transferability of results at an aggregate level, but also show that the barriers and corresponding mitigation measures can be context specific. In both, the design and implementation stages, the private participation led to different outcomes. The two largest cities in Ecuador each implemented three BRT corridors (1995 to 2013). The projects present similar characteristics, and thus a unique opportunity to analyze how private participation influenced their performance. The analysis identifies the level of private participation and to what extent it influenced the outcomes of the projects. Two approaches were identified: 1) Including incumbent operators by delegating vehicle acquisitions and operations; and 2) Replacing them with a public company. Financial strength and interest of the incumbent operators to continue functioning along parallel routes were major issues. Quito was successful in the implementation of the first corridor through public delivery, but failed its attempts with private participation. Guayaquil surmounted the barriers for effective private participation through a Special Purpose Vehicle (SPV) that managed the risk due to the inherent nature of the consortiums. This research provides the first comprehensive analysis of the design and implementation processes in Ecuador. It is expected that the results will contribute to more efficient and sustainable investments in the future.
Economic and Energy Impacts of Adaptive Reuse Building Construction
(University of Waterloo, 2019-08-29) Chan, Jacky; Bachmann, Chris; Haas, Carl
Adaptive reuse of buildings is an alternative to a building’s end-of-life where A building’s functional life may be extended to serve another purpose. Many studies suggest that adaptive reuse is more sustainable compared to typical demolition and new construction in terms of environmental, social, and economic impacts. However, these claims are qualitative in nature and are limited to economics at the project scale. This thesis quantifies the energy and economic impacts of adaptive reuse building construction in the Region of Waterloo (RoW) in Ontario, Canada. Input-Output (IO) models were developed to study the impacts of adaptive reuse building construction. First, an IO model was developed for Ontario. Then, it was regionalized into a two-region interregional input output (IRIO) model to study the RoW. The building construction industries’ intermediate inputs and final demands were altered in the Ontario IO model to reflect changes in the building construction industries due to changes in the supply and demand of adaptive reuse buildings. A basic scenario represents the situation where only the building’s superstructure and substructure are reused. The basic scenario was then extended to reflect the reuse of internal non-structural components. The IO models examine impacts to gross domestic product (GDP), industry outputs, employment and energy use, and comparisons are drawn between Ontario and the RoW. It was found that adaptive reuse building construction may benefit Ontario’s and the RoW’s economy and reduce energy consumption under certain combinations of changes in intermediate inputs and final demands. The desired domain of adaptive reuse construction, where energy use decreases, while GDP and employment increases, is discerned for both the residential and non-residential building construction industries in Ontario.
Economic Criticality of Ontario’s Highway Infrastructure
(University of Waterloo, 2017-05-02) Ashrafi, Zahra; Bachmann, Chris
Ontario is the economic center of Canada and hence generates a significant amount of freight activity. Moving forward, population growth coupled with growing and diverse trade strategies are likely to place new and increased demands on Ontario’s aging infrastructure. Since Ontario shippers and carriers rely on the mobility and accessibility provided by transportation systems, any restrictions or disruptions can have detrimental outcomes not only for the Province’s economy but also for the economy of Canada as a whole. Events that disable parts of the highway transportation network, ranging from weather conditions to construction closures, may affect freight travel times and ultimately threaten economic productivity. While previous studies of criticality typically focus on the impacts of natural disasters or terrorist attacks on system-wide travel times, they have not quantified the costs associated with disruptions to the economy via the freight transportation system. This research quantifies the economic criticality of highway infrastructure in Ontario, Canada, using a new measure of criticality that determines the cost of highway closures in dollar values ($) based on the value of goods, the time delayed, and the associated value of time. Measured this way, criticality is correlated with truck volumes, but differs by considering the values of shipments and network redundancy, resulting in new insights to critical freight infrastructure. For example, due to the high redundancy of the highway network within the Greater Toronto Area (GTA), highways become more critical further away. Moreover, sections of Highway 401 located west of the GTA are found to be more critical than those located east of the GTA because of lower redundancy in the western portion of the network, despite carrying lower truck volumes. Finally, with the cost of these disruptions quantified in dollars, one can then calculate the monetary benefits of potential transportation improvements for comparison (i.e., cost-benefit analysis).
Improving the Fiscal Transparency and Sustainability of Public-Sector Transportation Infrastructure
(University of Waterloo, 2017-08-25) Terry, Jacob Ross; Bachmann, Chris; Casello, Jeffrey
Public-sector infrastructure funding is a complex and often unclear process. Cities operate most efficiently if there is an adequately-funded transportation network with which residents and visitors can easily travel from origin to destination. Building these networks comes with challenges, which are often rooted in funding concerns. Part of this may be due to deficient understanding among the public and in governments about how transportation is truly funded, due to the opaque nature of many elements of government funding flows. This research addresses a need for methods that improve the transparency and fiscal sustainability of transportation infrastructure. Financial flows across governments are disaggregated and mapped across four tiers of infrastructure – federal, provincial, regional, and local – using the circular flow diagram as the base framework, to provide an understanding of how governments receive and spend funds. Origin revenues for water and transportation infrastructure in Waterloo, Ontario are determined, then the benefit model, which links revenues with effective expenditures, is used to evaluate whether the origin revenue sources being used to pay for each infrastructure system is effective. A sustainability analysis is then conducted for Waterloo and Toronto, comparing road and transit funding against the rate of infrastructure growth. The results suggest that water revenue in Waterloo is well-linked to the infrastructure, roads and active transportation are somewhat well-linked, and transit is not linked as well. Roads could be better linked if technology is implemented that allows better tracking of independent vehicles in urban settings. Roads in Waterloo and Toronto were found to be somewhat sustainably funded, although they trended in opposite directions, and transit funding was found to be less sustainably funded in Toronto, and somewhat sustainably funded in Waterloo pending the completion of the ION rapid transit system.
A Rules-based Mode Choice Model using CHAID Decision Trees and Dynamic Transit Accessibility
(University of Waterloo, 2021-05-31) Feng, Devin; Bachmann, Chris; Parker, Dawn
Transportation mode choice models typically represent user decision making using utility-based mode choice models. However, utility models assume that users make compensatory trade-offs between decision variables to maximize their expected utility. The decision process literature raises alternative, non-compensatory theories that suggest people employ simpler, cognitively frugal heuristics in their decision making. Non-compensatory models, including decision tree classifiers, present an opportunity to test the effects of transit accessibility variables on mode choices and improve descriptions of mode choice behaviour. Dynamic forms of transit accessibility, which measure variations in transit service over time, may better capture heuristic perceptions of transit service quality. This research addresses the need to understand how dynamic transit accessibility (DTA) impacts mode choices, without compensatory decision process assumptions. First, this research develops DTA measures for the Region of Waterloo using General Transit Feed Specification (GTFS) transit schedule information to calculate travel impedance matrices for departures at every 5-minute interval of the day. Zonal mode shares are regressed against alternative DTA measures to analyze the effects of different destination types, time periods of aggregation, and statistical parameters of transit accessibility (i.e., mean and distribution over time). Based on the aggregate mode share predictive performance, a DTA metric is selected for analysis within a binary (transit and not transit) disaggregate mode choice model. Second, this research uses trip diary data to train and score a Chi-squared Automatic Interaction Detection (CHAID) decision tree classifier to represent and predict rules-based mode choice processes. Finally, the selected DTA metric is merged with the trip diary data and applied in another decision tree for comparison. The comparison between the two rules-based mode choice models is based on overall model accuracy, class recall, precision, and interpretability. Results from the decision tree classifier reveal that users apply heuristics in their transportation mode decision making, including lexicographic and aspiration-level based decision rules. User choices depend primarily on transit pass ownership, and non-transit-pass users consider the trip’s distance thereafter. Including DTA as an independent variable in the decision tree has a small but statistically significant effect: users only seem to consider DTA, a generalized location-based measure, if they do not own a transit pass and only after considering the trip-specific distance. Overall, the rules-based mode choice models report accuracies of roughly 84%; however, low precision in the transit predictions (i.e., many false positives) result in an overestimation of regional transit shares.
Semi-Automated Microscopic Traffic Flow Simulation Development Using Smart City Data
(University of Waterloo, 2021-08-24) Lei, Qiao; Bachmann, Chris; Fu, Liping
Microscopic traffic simulation models have been widely used by transportation planners and engineers for conducting various road network planning and traffic engineering tasks. Due to data limitations, traffic simulation models are often calibrated based on macroscopic traffic measures. Recently, emerging smart city sensor technologies are enabling continuous collection of large volume, high-resolution trajectory data of road users, making it possible to estimate some behavioral parameters of traffic simulation models directly from these data. This research is intended to explore this opportunity with the objective of developing a methodology to estimate traffic simulation model parameters from smart city data with semi-automated calibration procedures. A comprehensive set of calibration procedures are proposed, including both direct methods of estimating parameters from data and indirect methods of estimating some parameters using an optimization algorithm. Most of the proposed procedures are designed in such a way that they can be completed in a semi-automated way using simple Python scripts. The developed methodology is illustrated in a case study involving the calibration of a VISSIM model using an available dataset of vehicle trajectories - NGSIM (Next Generation Simulation) traffic data. While most parameters can be estimated directly from the dataset, some parameters from the selected parameter set are determined using a neutral neural network. The modelling results suggest that the best performing parameter set generates less than 10% error relative to the field measurements in term of travel time and speed.
Transit Benefit Index: A Comprehensive Index for Capturing Externalities in Transit Planning
(University of Waterloo, 2022-09-30) Rendel, Rudi; Bachmann, Chris
Transportation externalities plague all members of society with delays during travel, as well as healthcare costs associated with crashes, unwanted noise, and air pollutants. While attempts have been made to correct these externalities, they have generally focused on charging travellers for the use of roads or vehicles to fully capture their costs. However, the costs associated through private vehicles can instead be mitigated with the proper funding and support of public transportation systems, which can mitigate the number of private vehicles on roads. Past literature has shown that when users are charged for the use of roads and vehicles, a region’s social welfare can decrease, but an increase in subsidies and funding towards public transportation can provide similar benefits and a competitive alternative to users who primarily make private automobile trips. This research provides a methodology and tool (Transit Benefit Index) for estimating the total societal benefit generated from the substitution of private vehicle trips with public transportation trips. The external costs of private and public transportation trips are calculated in a base case travel demand model and then a mode shift is simulated to calculate the effects of shifting one full transit unit of demand from private to public modes. This shift is performed for all Origin-Destination (OD) pairs in a city or region to find the OD pair which results in the greatest net benefit. These benefits are then normalized using the total vehicle kilometers travelled removed from the network to generate a “Transit Benefit Index”. This methodology is applied to a case study using a travel demand model provided by the City of Bogotá, Colombia. Bogotá was selected due to its impressive BRT system and overall transit connections as well as its prevailing problems with congestion. A total of 12 scenarios were simulated: a base case, and 5 sensitivity analyses, each including two different transit provision per case. The results indicate that the expected savings from shifting travellers from private to public modes provides a greater economic benefit than the cost of a transit ticket in Bogotá. The conclusions are further extrapolated to the cost of a new transit unit, and it is found that the benefits derived from increased transit ridership could recover the cost of a new bus within a year. These results suggest that the City of Bogotá should consider further subsidies to transit fares to generate more transit ridership.
A Transport-Economic Analysis Framework for Border Crossing Infrastructure Investment Policies
(University of Waterloo, 2019-09-30) Shahrokhi Shahraki, Hamed; Bachmann, Chris
Border crossings serve two critical purposes: ensuring the safety and security of a nation; and facilitating trade and movement of people between countries. Inefficient border crossings resulting from insufficient infrastructure investments create bottlenecks to economies. Despite the importance of border crossings, studies aimed at optimizing border crossing investments are limited. This thesis introduces an innovative transport-economic modelling framework to optimize border crossing infrastructure investments. The framework migrates from a stylized CGE modelling approach by explicitly linking transportation models of border crossing activities to a Computable General Equilibrium (CGE) model of the global economy. The framework combines the capabilities of a CGE model with several transportation models and datasets to determine border crossing investment priorities. The framework addresses some of the limitations of prior studies in the literature by incorporating queuing theory and mode choice theory to comprehensively measure the economic impacts of border crossing investments. The developed framework is applied to Canada-US border crossings to determine short- and long-term border crossing investment priorities. Simulation results suggest that reducing delay times at border crossings can have sizeable impacts on the Canadian economy. The impacts on Canada’s GDP and welfare are always positive and can range up to $ 92.44M USD and $ 79.83M USD per year, respectively. The impacts of infrastructure investment on the export of Canadian industries varies from a reduction of $ 0.86M USD to an increase of $ 8.47M USD per year. Analysis results suggest that Ambassador Bridge, Sarnia, and Fort Erie are the three most important borders for Canadian economy. The analysis results suggest that the magnitude of the effects of border crossing investment and the border crossing investment priorities are highly sensitive to border crossing delay modelling and less sensitive to mode shifts resulting from investment in one mode of transportation. This research concludes with border crossing priorities and the policy implications.
Valuation of in-situ Building Materials for Resource Recovery
(University of Waterloo, 2024-02-08) Mollaei, Aida; Haas, Carl; Bachmann, Chris
The construction industry is among the largest contributors to global raw material consumption and is responsible for 40% of annual greenhouse gas emissions. Recovery of building materials at the end of a building's life, often seen as a common circular approach, can help mitigate the environmental impacts within this sector. However, the feasibility of recovering in-situ building materials is dependent on various technical, operational, financial, environmental, and regulatory factors, making the implementation of resource recovery complex and challenging. The main objective of this research is to develop methodologies that improve the recovery of building materials at end-of-life through assessment of the value of in-situ building materials. At the core of this research, a decision support tool is developed that incorporates the main factors that impact the value of materials embedded in buildings. The tool is designed based on a multi-objective optimization model that estimates optimal end-of-life options for building components. Throughout this research, the tool is applied to various case studies and analyzed through sensitivity analyses. Using the developed tool, a novel methodology is proposed to assess the efficacy of policies focused on deconstruction and building recovery. Following that, the impact of regional factors such as labour costs, material markets, and socioeconomic factors, are assessed on building end-of-life strategies. The findings underscore the necessity of tailored policies and regulations to effectively reduce waste generation within specific regional contexts. Finally, expanding the applicability of the developed tool on future building stocks, a methodology aimed at evaluating circular design and construction strategies on the recovery potential of buildings is provided. This thesis contributes to the development of optimized material recovery processes that result in waste reduction and carbon emission mitigation. Realizing the recovery potential of building materials is a pivotal step towards fostering a more circular construction sector.

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