193 research outputs found
Optimum Allocation of Transit Signal Priority Deployment Along a Transit Corridor: A Bilevel Optimization Approach
No full textThe central focus of this thesis is to develop a delay cost optimization model based on the cost of total person delay determining optimal Transit Signal Priority (TSP) configuration along a specified corridor based. The optimal configuration of TSP along a corridor allows for TSP to be implemented when it only provides cost benefits and reduces overall delay. Additionally, if there is an implementation or operation restriction for the number of intersections with TSP enabled then this optimization model allows for the immediate selection of the optimal locations. The TSP configuration model as a bilevel approach with the upper level expressed as a delay cost optimization model is a useful tool for current and future transit planning applications. It uses newly available data and provides thorough recommendations for the optimal configuration of TSP based on selected key performance indicators (KPIs) including threshold values for each intersection. The cost delay optimization model was developed to calculate the cost of delay as related to TSP implementation at each intersection along a corridor using KPIs were selected to represent all users of the corridor for peak hour flow, specifically current bus passengers, downstream waiting passengers, and other commuters driving private vehicles. The bilevel approach can be split into the upper-level delay cost optimization model and the lower-level VISSIM model. The upper-level delay cost optimization model may be used to evaluate each intersection along a corridor as to the efficacy of implementing total person delay TSP and is developed from selected KPIs. The cost delay optimization model to minimize delay of all users along a corridor was developed based on the selected KPIs as the input variables. The output of the delay cost optimization model is the configuration of TSP along the corridor which is then input into the VISSIM model during the bilevel approach
Exploring Travel Behavior and Activity Patterns using Urban Transit Mobility Sensing Data
No full textIn this study, we employ a probabilistic topic modeling algorithm, known as Latent Dirichlet Allocation (LDA), to autonomously deduce the purposes of trips based on activity characteristics extracted from smart card transit data. While the majority of existing literature has primarily concentrated on identifying patterns related to home and work-related activities, our research delves deeper into the realm of non-home and non-work activities, aiming to uncover distinctive patterns associated with a more granular spectrum of activities. Temporal attributes of activities are derived from trip data recorded by the Tehran subway's automatic fare collection system. Furthermore, we enrich the spatial attributes of non-home and non-work activities by incorporating land-use data. Multiple activity attributes, including start time, duration, frequency, and land-use information, are harnessed to infer activity purposes and patterns. Our analysis uncovers 14 distinct patterns associated with non-commuting activities, based on their temporal and spatial characteristics. These patterns encompass educational, recreational, commercial, health, and other service-related activity types. To gain further insights, we analyze changes in passenger trip patterns and behaviors before and during the COVID-19 pandemic, with a specific focus on non-home and non-work-related activities. Our investigation reveals significant alterations in these patterns. For instance, we observe a reduction in both the number and duration of recreational patterns, alongside the elimination of morning patterns in educational activities. Moreover, the number of commercial activities has decreased. The proposed model effectively captures shifts in travel behavior triggered by various disruptions, making use of smart card transit data. This capacity holds the potential to facilitate travel demand modeling, inform future planning and system management, and enable more adaptive decision-making processes
Freight Trip Generation Modeling at the Establishment Level Using GPS and Point-of-Interest Data: A Case Study in Alberta
No full textFreight trip generation (FTG) modeling is essential for understanding urban freight activity, yet traditional approaches rely on static trip rates that fail to capture variations across industry sectors. This thesis develops an establishment-level FTG model by integrating truck GPS trajectory data with point-of-interest (POI) data, addressing limitations in conventional survey-based methods. The study focuses on Edmonton and Calgary, Alberta, leveraging a dataset of over 19.5 million GPS pings from heavy-duty trucks and detailed establishment attributes such as employment size, sales volume, and North American Industry Classification System (NAICS) codes. Four regression models, namely Linear, Lin-Log, Power, and Exponential, are applied to estimate FTG and evaluate the predictive strength of different explanatory variables. Results indicate that while employment size is a strong predictor, incorporating sales volume improves model accuracy in some industry sectors. A critical aspect of this research is the assessment of spatial transferability, determining whether FTG models developed for Edmonton can be applied to Calgary with comparable predictive performance, though regional economic differences influence adaptability. This study contributes to FTG modeling by demonstrating the feasibility of GPS and POI data fusion, reducing reliance on costly surveys, and improving predictive accuracy. The results offer valuable insights for policymakers and freight planners seeking scalable, data-driven approaches to urban freight management. Despite these contributions, certain limitations remain. The absence of commodity-level data restricts insights into shipment characteristics, and model transferability may be affected by regional economic variations. Addressing these limitations in future research could further enhance the accuracy and applicability of FTG models. Future research should explore advanced machine learning techniques and additional explanatory variables to refine FTG models and improve their adaptability across urban contexts
Travel Behavior Analysis and Mode Choice Prediction for Commuting to Campus - Performance Comparison of Discrete Choice and Machine Learning Models
No full textExtensive studies exist on various aspects of travel behaviour of the general population; however, few works explore the commuting behaviour of university commuters. This thesis investigates university commuters' commuting habits/attitudes to better understand their commuting behaviour and compares predictive models' performances on university commuters' transportation mode choices. For this research, an online survey was administered in March 2020 among the University of Calgary members to shed light on the current travel choice preferences of the university commuters, investigate factors affecting their mode choice, examine their satisfaction level toward various modes, and uncover ways to encourage more sustainable transportation. Further, the data is used to develop traditional discrete choice models and novel machine learning algorithms to predict commuters' transportation mode choice and examine the importance of various factors on their mode choice. The aggregated transportation mode share of the survey respondents shows that 57.89% of survey respondents are either public transit or active mode users, indicating a high percentage of sustainable transportation mode users among university commuters. Various characteristics of survey respondents were shown to be important in their travel behaviour, such as socio-demographic, household and geographical location. It is shown that both age and income level positively affect car usage while negatively affect public transit usage. The geographical analysis also indicates that travel distance and accessibility to transit facilities influence university commuters' mode choice decisions. As travel distance increases and accessibility to transit facilities decreases, university commuters prefer to use more cars than public transit and active modes. Further, it is shown that university members have various satisfaction levels of using different transportation modes and consider various barriers to use them. It is shown that university status and socio-demographics affect commuters' attitudes toward satisfaction level and barriers to use sustainable modes. Overall, employees are shown to be highly concerned about the environmental footprint of using cars; although, they are mainly car commuters. In contrast, students are determined to be more concerned about driving costs and parking availability. In terms of transit, travel time commuting and consistency are determined to be common concerns of university commuters. Similar to transit, the weather condition is determined to be the most important concern for using active modes. The results of the descriptive analysis are further used for policy recommendations to different university administrations to encourage more use of sustainable transportation. Besides descriptive analysis, the prediction performance of various machine learning classifiers is compared with the traditional multinomial logit model on predicting the University of Calgary commuters' transportation modes. The comparison results show that the Extreme Gradient Boosting (XGBoost) method performs better in travel mode choice prediction of the university commuters for higher overall accuracy and F1-score. In addition to the performance comparison, this thesis estimates the relative importance of explanatory variables on various models and shows how they relate to mode choices. This research shows that travel-related information is more influential on machine learning algorithms, while socio-demographic and household characteristics have more effect on the utility functions of the multinomial logit model
Optimization of Autonomous Goods Delivery Systems in Urban Areas
No full textThe freight sector is growing: disruptions in this sector have accelerated and are coming under more public scrutiny than other technological changes in the past. Autonomous goods delivery technologies are such a disruption, vehicles with no human on-board that can travel along roads, sidewalks, or in the air. Without the constraint of a human driver, many different forms and functions of vehicle can be created. It is uncertain the type or design of autonomous vehicle that will be best suited for exactly what scenarios, the strategies that will be used to operate them, and the supporting infrastructure changes that will be needed to best accommodate them. This thesis provides planning tools for this change that industry and government can utilize for better, quicker, and more transparent decisions. These methods also allow for sensitivity analysis and the impacts of technology changes to be considered and investigated. The impacts that uncrewed aerial vehicles (UAVs) and sidewalk autonomous delivery robots (SADRs) will have on the optimal locations of micro-fulfilment centers (MFCs) and mothership vans (MSs) are analyzed. It is found that the sensitivity of the relationship between stock-turnover by area and the optimal number of MFCs is a barrier to the adoption of UAV and MFC systems. Moreover, delivery time-windows are shown to be a primary motivating factor in the adoption of UAVs and the areas of a region that are most cost-effective for the switch to UAV and MFCs are the areas furthest from existing logistics centers. For SADRs working with MS, two distinct deployment strategies are identified and compared, then the breakeven cost points for these strategies are analytically determined and expressed in a decision matrix table. A series of sensitivity analysis scenarios shows how exurbs and further suburbs of urban areas are most cost-effective to move to MS with SADR systems. Finally, UAVs and SADRs are compared simultaneously, and a mixed fleet system considered and optimized with conventional vans. The numerical study of Singapore shows that increasing the autonomy of UAVs and SADRs is a key barrier for more widescale adoption
Dynamic Shared Autonomous Vehicle Fleet Operations with Consideration of Fairness
No full textThe future of urban transportation has arrived, and it is moving in the direction of enabling urban mobility platforms to provide shared mobility services, accelerating the shift away from personal vehicle ownership. New companies, like Uber and DiDi, are heavily investing in developing and testing emerging mobility technologies, including shared autonomous vehicles (SAVs). The full implementation of emerging mobility technologies is expected to deliver a transformative wave of urban reform. Besides, emerging mobility technologies could offer promising sustainable solutions that would optimize the usage of limited mobility resources. For instance, shared mobility services are convenient, flexible, cost- and time-efficient, and environment-friendly. Further, fully-autonomous vehicle (AV) technology surpasses human drivers in terms of costs, driving behavior, hours of service, and compliance with the plans of fleet operators. Currently, researchers are extensively studying the operations of SAV fleets that provide on-demand curb-to-curb mobility services. Specifically, they develop traveler assignment and scheduling algorithms that aim to match each traveler with a proper vehicle and plan the schedule of the vehicle simultaneously, including picking-up and dropping-off other travelers, based on a specific fleet objective. This thesis aims to fill an existing gap in the literature regarding introducing “equitable” methods to dynamic ride-sharing (DRS) systems. Thus, to meet the rising concerns of social justice, equity, and fairness in transportation systems, this thesis introduces the proportional fairness concept to DRS systems while considering the passenger heterogeneity in terms of their valuation of in-vehicle travel time. The proportional fairness formulation seeks to balance efficiency and fairness in resource allocation problems. The proportional fairness approach is then compared to two other approaches in a simulation-based environment implemented in MATSim (i.e., an agent-based transport simulator). In a centralized-fleet setting, the first approach aims to maximize traveler utility/satisfaction, while the second approach aims to maximize the total travelers’ utility. Simulation scenarios are tested to quantify the trade-offs between fleet size and vehicle maximum allowable occupancy. The performance of the three approaches is evaluated based on various performance measures from a fleet management perspective [e.g., the ratio of zero-occupant (i.e., empty-vehicle) fleet kilometers traveled to total fleet kilometers traveled], a traveler perspective (e.g., the average traveler wait time), and equity in resource allocation perspective (i.e., the Gini coefficient)
Long Term Planning and Modeling of Ring-Radial Urban Rail Transit Networks
No full textExtensive work exists on regular rail network planning; however, few studies exist on the planning and design of ring-radial rail transit systems. With more ring transit lines being planned and built in Asia, Europe and the America’s, a detailed study on ring transit lines is timely. This thesis is based on idealizing transit network in perfect ring-radial transit lines. An analytical model using the continuum approximation approach is first introduced to find the optimal number of radial lines considering a city with a radio-centric street network. An approximate analytical model for ring-radial rail network planning is then introduced allowing analysis of the feasibility and optimal alignment of a ring transit line in a city. The city of Calgary‘s light rail transit network and Shanghai metro network are used to illustrate the applicability and transferability of the model. The model is then extended to allow simultaneous consideration of radial and ring lines and analyzing a transit network with partial ring and radial lines. This extension allows a more realistic idealization and analysis of rail transit networks. A benchmark analysis of cities with ring transit lines is used to identify prominent types of lines in idealized ring-radial transit networks. The cities are then assessed based on their unique network patterns using identical model inputs such as length of rail transit network and trip distribution patterns. This thesis provides a decision support tool for transit planners to compare the performance of different rail transit network extension alternatives for long-term rail transit planning. It can also be used for cost- benefit analysis to compare total generalized passenger cost savings versus the cost of network extension. Unlike simulations and agent-based models, this model is shown to be easily transferable to many ring-radial transit networks. Therefore, with a daily OD trip matrix and transit network supply characteristics and parameters as input, the model can be implemented for many radio-centric cities. The benchmark analysis using the combined universal ring-radial rail transit network model is a mathematically sound platform to compare different rail transit networks and propose the best examples of rail network topologies
Development of a probe-based proactive coordinated ramp metering approach
No full textBibliography: p. 79-8
Author Response
No full textRahmatinejad Z, Hoseini B, Pourmand A, Reihani H, Rahmatinejad F, Eslami S, Author Response. Indian J Crit Care Med 2024;28(2):183-184
Level of Service Measures for an Urban Bus Route
No full textThe ability to measure the level of the quality of transit service provided is of utmost importance for customers to assess the level of service they receive and for the transit agency to assess the effectiveness of the service improvements made. Despite its importance, the transportation industry lacks an efficient, widely accepted, and widely applicable overall level of service (LOS) measure. Specifically, one that can assess and compare the overall quality of service (QOS) of transit lines or systems or one that can compare different operational performances of the same transit line or system is needed.
The content of the thesis consists of four major parts. The first part critically reviews major domains of transit level of service (TLOS) measures in industry and academic literature. It focuses on the success in achieving anticipated goals as opposed to the requirement of such a measure. Existing measures fall short in incorporating a combined view of both the passenger and operator and in assessing the overall TLOS by a single measure. A new approach to evaluate TLOS is proposed that has the potential to address these drawbacks.
The second part of the thesis proposes a novel approach to measure the LOS with respect to the value of time (VoT) distribution of the passengers. An implied VoT representing the LOS of a particular attribute, a combination of attributes, or overall service is derived and is compared with the respective VoT distribution of the passengers to obtain the LOS. An approach to distinguish LOS grades depending on the standard deviation (SD) of the VoT distribution is proposed.
The third part of the thesis engages in developing three LOS measures representing five attributes of concern in the thesis. Accordingly, a measure to represent headway and crowding attributes, a measure to represent access and travel time attributes, and a measure to represent the reliability attribute are developed. Each measure represents an implied VoT figure obtained by simulating an existing operation using an analytical model of optimum operation related to the service attributes of concern. The analytical model of optimum operation is developed from the basics for reliability LOS measure, while for other measures, existing models in the literature are modified and used. Finally, the three measures developed are combined using a novel approach to represent the overall LOS of a bus route. The development of each LOS measure is accompanied by a numerical example explaining the calculation of the LOS of a bus route.
The fourth and final part of the thesis applies the developed measures to a bus route operation in Calgary. The data for the bus route is obtained from Calgary Transit for the year 2021. While each chapter discusses the derived LOS measure and draws conclusions, the final chapter provides insights into potential improvements to the suggested approaches and potential future research related to the developed work
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