Browsing by Author "Saidi, Saeid"
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- ItemOpen AccessAssessing Alternative Optimum Bus Operations Strategies Considering Route Demand, Pattern, and Crowding(2022-08) Heidarigharehsoo, Negar; Saidi, Saeid; Wirasinghe, Sumedha Chandana; De Barros, Alexandre GomesThis thesis developed four mathematical models using continuum approximation and applied them to an urban bus route. Alternative operating strategies are compared to conventional all-stop operations. The comparison of skip-stop and express-local schemes with stop skipping designs and on-demand strategies with flexible stopping patterns are conducted to determine the most efficient bus operating service under various conditions. Each alternative has a total cost that includes walking time, waiting time, in-vehicle travel time, and transferring between lines, in addition to operating costs. This thesis also considers the impact of the COVID-19 pandemic on public transit. As a result, we incorporated the crowding disutility based on the loading factor and the denied boarding costs into the optimization models. First, we solved the theoretical case and determined the most efficient bus operating strategy for various ranges of passenger demand and average trip length. Next, we solved the continuous optimization models by optimizing bus headway and stop spacing. Additionally, this thesis conducted sensitivity analyses of various conditions, including determining the most efficient strategy under fleet size constraints as well as the sensitivity of passengers to crowding and travel times using numerical examples. The solution proposed in this thesis is responsive to changes in demand, trip patterns, and passenger sensitivity to cost components. The model is applied to a bus route in Calgary, Canada, and provides an optimal bus dispatching scheme for two scenarios, with and without considering crowding discomfort. Results show that on-demand services have the lowest generalized costs in scenarios with low demand. In the case of higher demand and longer trips, conventional all-stop systems are preferred. Under high demand and longer passenger trips, skip-stop and express-local services can lower overall system costs. Considering crowding measures, the lowest cost alternative option shifts from conventional services to strategies with stop-skipping designs, such as skip-stop and express-local policies. Express-local strategy dominates other services when the fleet size is limited, and crowding is considered.
- ItemOpen AccessDevelopment of a probe-based proactive coordinated ramp metering approach(2010) Saidi, Saeid; Kattan, Lina; Hall, Fred
- ItemOpen AccessDynamic Shared Autonomous Vehicle Fleet Operations with Consideration of Fairness(2021-04-27) Habib, Nouran; Kattan, Lina; Alp, Osman; Waters, Nigel; Saidi, SaeidThe 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).
- ItemOpen AccessExploring Travel Behavior and Activity Patterns using Urban Transit Mobility Sensing Data(2023-12-13) Aminpour, Nima; Saidi, Saeid; Demissie, Merkebe; De Silva, DimanthaIn 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.
- ItemOpen AccessImpacts of Weather on Urban Bus Performance in the City of Calgary, Alberta(2023-02-23) Mohammadi, Mohammad; He, Jennifer (Jianxun); Saidi, Saeid; Zhou, Qi; Waters, NigelThere is an extensive body of literature on the impacts of adverse weather on traffic performance and transit ridership; however, only a few research have investigated the impacts of adverse weather on urban bus performance. Traffic congestion and unfavourable road conditions caused by adverse weather directly affect the performance of buses. Also, adverse weather impacts buses indirectly by affecting the passengers. This study aims to evaluate the impacts of adverse weather (rainy and snowy weather) on urban bus performance in the City of Calgary. This research focuses on the impacts of rain and snow on seven bus routes in the City of Calgary. Calgary Transit provided the automatic vehicle location and automatic passenger counter data for 2019-2021. The weather data was supplied by the Calgary International Airport and included daily snowfall and 5-minute rainfall data from 16 rain gauges along the bus routes. Statistical tests and public transit performance measures have been employed in this study to assess the impacts of rainy and snowy weather on the buses. The Man-Whitney test was used to identify significant changes in the median of ridership, run time, dwell time and travel time. In addition, Levene’s test was employed to capture significant changes in the variance of run time, dwell time and travel time. Moreover, six measures from four categories of public transit performance measures (schedule adherence, headway, travel time, and wait time) were used to evaluate the impacts of rainy and snowy weather on bus performance. On-time Performance, coefficient of variation of headway, service regularity, coefficient of variation of travel time, 90th-50th percentile travel time, and excess wait time were all used to determine the effect of rain and snow on bus performance. This study found that there is a negative impact of rainy and snowy weather is definite on bus performance. However, the level of impact varies by route and data groups, which suggests considering other influential factors on the performance of buses along with weather for more detailed results.
- ItemOpen AccessLevel of Service Measures for an Urban Bus Route(2023-07) Wimalasiri Devasurendra, Kaushan; Wirasinghe, Sumedha Chandana; Kattan, Lina; Saidi, Saeid; Waters, Nigel Michael; Nowicki, Edwin Peter; Mehran, BabakThe 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.
- ItemOpen AccessLong Term Planning and Modeling of Ring-Radial Urban Rail Transit Networks(2016) Saidi, Saeid; Wirasinghe, Chan; Kattan, Lina; Schonfeld, Paul; de Barros, Alex; Ruwanpura, Janaka; Waters, NigelExtensive 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.
- ItemOpen AccessOptimal Route Planning for Parking Enforcement Patrol using Reinforcement Learning(2023-11-22) Alemi, Ali Reza; Saidi, Saeid; Sabouri, Alireza; Kattan, Lina; Zangeneh, Pouya; Black, KerryWith the considerable population growth in cities, the need for sustainable and feasible parking enforcement solutions becomes increasingly important. A Parking enforcement solution involves finding optimal patrol policy for enforcement agents. A Patrol policy refers to a strategy or a plan for how patrols should be conducted in areas with potential violations to prevent violations and improve parking agency compliance. Given a comprehensive database about violation's distribution in different parking locations, we can incorporate an optimization model to find optimal patrol policies for different agents. However, in an environment in which we do not have such a complete database and also drivers change their attitude towards parking fee payments frequently, the effectiveness of a parking enforcement solution is measured by how it can effectively address the uncertainty existing in the number of violations for different locations. The effectiveness and efficiency of patrol enforcement algorithms have been argued in the literature. Still, the solution proposed in this study aims to tackle the problem using learning algorithms that were rarely used in previous works. We consider the problem of finding an optimal routing plan for the parking enforcement patrol vehicles when only partial data about the distribution of violations over the city is available. The decision maker faces the well-known exploration-exploitation trade-off, i.e., choosing the best route given the current information or trying new routes to gather data on potentially better routes. In the absence of a learning-based algorithm, an optimal patrol policy can only be considered as optimal regarding the current state of the environment's features but if the environment's features change, the previous solution is no longer optimal. A learning-based algorithm aims to learn the dynamic features of an environment and construct the optimal patrol policy according to the learned features. In this thesis, we first describe the problem and different approaches for the proposed problem; then, we propose a multi-arm bandit formulation and use reinforcement learning to sequentially generate routes to maximize the system's expected reward. Next, we will analyze the performance of our framework against the current patrol policies being conducted in the city. During this study, an interactive dashboard is developed and used throughout the study for spatially analyzing the distribution of violations across the city. This tool is adaptable for any agency looking into the spatial analysis of violation patterns. Our analytical findings indicate a potential increase in the observed number of violations with the implementation of this framework which leads to the agency's compliance improvement. In the final section, we will discuss the contribution and expected outcomes of the study in detail.
- ItemOpen AccessOptimum Allocation of Transit Signal Priority Deployment Along a Transit Corridor: A Bilevel Optimization Approach(2023-12-15) Grzyb, Amelia Lauren Selina; Kattan, Lina; Saidi, Saeid; Nur Kutlu, SuleThe 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.
- ItemOpen AccessTravel Behavior Analysis and Mode Choice Prediction for Commuting to Campus - Performance Comparison of Discrete Choice and Machine Learning Models(2021-09-21) Kamkar, Hassan; Saidi, Saeid; Farhan, Ali; Demissie, Merkebe GetachewExtensive 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.