Browsing by Author "Demissie, Merkebe Getachew"

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    Assess the Equity Impacts of Different Transportation Modes using Access to Health Facilities for Asthma Patients as an Indicator
    (2024-12-20) Minuyee, Mahnoush; Demissie, Merkebe Getachew; Behjat, Laleh; Blue, Gwendolyn; Paul, Robyn Mae
    Asthma, a chronic respiratory condition affecting 10\% of Canada’s population in 2021, underscores the critical need for equitable and timely access to health facilities, particularly during emergencies. Transportation systems are vital for enabling access to these services; however, socioeconomic disparities and transportation limitations often restrict access for vulnerable populations. This thesis examines how transportation modes influence equitable access to health facilities for pediatric asthma patients, using Calgary as a case study. It explores a framework to improve transportation equity across traffic analysis zones to better serve disadvantaged groups. Two methods were employed to measure accessibility: the reachability-based method and the two-step floating catchment area method. Accessibility indexes were calculated for Calgary’s traffic analysis zones across three transportation modes—personal vehicles, public transit, and taxis—considering five-time slots during the day for emergency and non-emergency scenarios. Different travel thresholds and budgets were analyzed to identify inequities across zones. Equity was evaluated using horizontal and vertical equity techniques. Horizontal equity was assessed through the Gini coefficient, while vertical equity considered socioeconomic factors, pediatric asthma patients, and accessibility indexes to prioritize zones needing improved access to health facilities. The findings show that personal vehicles provide the highest access to health facilities across most time slots and travel budgets, making them the primary mode for accessing health facilities, especially in emergencies. Taxis are unaffordable at low budgets, while public transit often fails to offer sufficient accessibility during emergencies, particularly at night when available health facilities are limited. In non-emergency scenarios, taxis offer comparable accessibility at higher budgets. Vertical equity analysis highlighted zones with significant socioeconomic vulnerability and high asthma rates but low access as priorities for action. This research emphasizes the importance of incorporating equity into transportation planning to enhance public health outcomes. Policymakers should adopt a framework to prioritize underserved zones with greater accessibility needs while addressing both horizontal and vertical inequities. Future studies should address limitations by including weekend data and health facility capacity to provide a more comprehensive analysis of accessibility needs and their equity impacts.
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    Change and the City an alternative policy development framework to improve the quality of transportation policy in Calgary
    (2024-06-26) Whyte, Robert Vincent; de Salvatierra, Alberto Embriz; Demissie, Merkebe Getachew; Alaniz Uribe, Francisco; Plitt, Robert; Burda, Cherise
    Planning policy is the language that translates governance into built form. It is a necessary and integral tool in the operation of democracy. This doctoral thesis examines transportation planning policy in the Alberta context as a means for investigating the constituent pieces of good and bad planning policy. Using design science research, autoethnography and interviews as components of a research program, this study explores the state of transportation planning policy to better understand how policy has and continues to shape the communities that it guides. This work attempts to highlight the reasons and conditions behind why policy may succeed and use that information to propose an improved way of developing planning policy. Common failures in policy and policy implementation are examined and strategies proposed for practitioners to use. Through the research, the concept of a policy life-cycle and the adaptation of a policy development cycle are described and defined as new concepts in the practice. An examination of policy successes, failures and the conditions that lead to those outcomes shapes a root cause analysis which forms a basis for work. The information is then used to build, test and refine creative artifacts through the research project. A policy development guidebook is generated as a final output and tool that policy planners in Alberta can use to improve the success rate of their municipal planning policies.
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    Data-driven Approach for Assessing Urban Road Network Resilience: Integrating Spatiotemporal Analysis with the Resilience Triangle Concept
    (2024-01-18) Azargoshasbi, Rouzbeh; Kattan, Lina; Demissie, Merkebe Getachew; Kutlu, Sule Nur
    Urban mobility is constantly challenged by congestion and unpredictable disruptions, making it more crucial than ever to understand and improve the resilience of urban road networks. Growing cities have necessitated reliable and efficient transportation systems, highlighting the importance of a thorough resilience study that analyzes the dynamics and complexities of road networks. Improving resilience is not only about addressing short-term traffic problems; it also aims to ensure the long-term sustainability and adaptability of urban infrastructure. This thesis presents a data-driven approach to investigate the spatiotemporal impact of daily non-recurring disruptions and the resilience of urban road networks. The underlying traffic propagation dynamics and recovery time, vulnerability, and resilience of an urban road network are examined using multi-year observed travel time and incident data. The study develops a statistical method to estimate event occurrence, restoration, and recovery times and formulates a new resilience metric inspired by the resilience triangle concept and complex network theory. The analysis captures the microscopic dynamics of affected road links in detail and allows for an accurate estimation of an incident’s occurrence, restoration, and recovery time. The results indicate that incidents are often detected earlier than reported, but the impact of those incidents remains on the network for a longer period than reported. In addition, areas with low resilience tend to be geographically clustered, often near high-demand regions that have low network densities, indicating inefficiency in the network and low resilience. This study demonstrates that the proposed methodology captures network responses to disruptions accurately and provides valuable insights for transport policy, including the strategic placement of recovery resources, such as police units, during disruptions. The findings of this study have significant implications for the improvement of urban road network resilience.
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    A Hybrid Macro-Microscopic Speed Harmonization Model in a Connected Autonomous Vehicle Environment - A Model Predictive Control Approach
    (2022-01-07) Chakas, Kinda; Kattan, Lina; Demissie, Merkebe Getachew; Waters, Nigel Micheal
    Lane changing activity has been closely related to capacity degradation of congested freeways near bottlenecks leading to traffic breakdown. This flow reduction witnessed with the increase of lane changing is mainly attributed to speed variations between lanes, speed drops and vehicles’ sluggish acceleration when moving from a slow to a faster lane. Thus, implementing speed harmonization (SH) with prediction of lane changing as a proactive control strategy is effective in preventing freeway capacity drop, especially with the advent of connected and autonomous vehicle technology (CAV) and its inherent continuous capability of collecting and disseminating its individual vehicle trajectory data. This research develops a model predictive SH control that aims at improving bottleneck throughputs while reducing discretionary lane changing. The SH is developed for a mixed environment of CAVs and human-driven vehicles (HVs). The core of this developed strategy is the integration of a lane changing model with a stochastic car-following model to devise a proper speed limit for individual CAVs, thereby suppressing shockwave propagation. The predictive SH strategy is developed as a hierarchal control strategy using both macroscopic and microscopic models to obtain the optimal length of the SH control section and the optimal speed of CAVs with a speed-difference dampening effect. The viability and efficiency of the proposed framework are demonstrated via numerical simulations for different levels of market penetration rates of CAVs. It is found that the SH control strategy can reduce the total travel time by reducing both vehicle-queuing at the bottleneck as well as lane changing maneuvers; meanwhile hedge against the backward shockwaves and, therefore, can smooth traffic. The average travel time is reduced by 10.86%, 16.78% and 25.28% for scenarios 30%, 40%, and 50% CAVs penetration rate, respectively, in case of SH control with model predictive control (MPC) based on CAV and HV behaviour. Moreover, a sensitivity analysis revealed that a latency in receiving CAV data can significantly decrease the efficiency of the SH control algorithm, especially at a low % CAV penetration rate, and that a penetration rate of 37% is sufficient in mixed traffic. As a result, CAV information can replicate the whole traffic behaviour without the need to estimate HV behaviour.
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    Integrating Queue Dynamics into the Trip-Based Macroscopic Fundamental Diagram
    (2025-01-15) Hassanin, Omar; Kattan, Lina; Demissie, Merkebe Getachew; He, Jianxun (Jennifer)
    Macroscopic traffic models provide a simplified framework for analyzing and controlling traffic at the network level. Among these models, the trip-based macroscopic fundamental diagram (MFD), or generalized bathtub model, effectively captures inflow, accumulation, and outflow while considering trip lengths and travel times, particularly under rapidly changing traffic conditions. This study addresses a gap in the trip-based MFD, which lacks queuing dynamics under downstream restrictions, as its original outflow function assumes unrestricted flow. To resolve this, the research incorporates downstream queuing dynamics, accounting for waiting times and their changes over time. The study also examines the impact of connected and automated vehicles (CAVs) on traffic dynamics through numerical simulations. Assuming that CAVs improve the MFD shape and bottleneck capacity, a sensitivity analysis was conducted for various market penetration rates (MPRs). Key findings include: 1) Average upstream waiting time decreased from 130 seconds to 0 at 60% MPR; 2) Average reservoir travel time reduced by 60% (570 to 230 seconds) at 100% MPR; 3) Average downstream waiting time initially increased by 25% at 70% MPR but fell by 12.5% at 100% MPR; and 4) Average total trip time (TTT) dropped by 61%, from 780 to 300 seconds. The results show earlier commuter exits, shifting the peak time (𝑡𝑝) of TTT. However, as 𝑡𝑝 depends on external factors (e.g., work schedules), the inflow demand pattern was adjusted to maintain 𝑡𝑝 constant, revealing a 26-minute inflow peak shift at 100% MPR. Additionally, congestion duration reduced by 51%, from 96.6 to 46.6 minutes, while maximum travel time decreased by 66% (18.5 to 9.5 minutes). Lastly, previous enhancements in capacities maintained constant free-flow speed (𝑣𝑓) for safety reasons. However, when the MPR reaches 100% and there are no longer any human-driven vehicles, increasing 𝑣𝑓 no longer poses safety risks. Thus, 𝑣𝑓 can reach 21m/s instead of 15 m/s, leading to less congestion. The findings demonstrate that CAVs significantly reduce trip time and congestion but cannot alone eliminate the negative impacts of downstream bottlenecks.
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    Network-Wide Route Guidance with Consideration of Fairness: A Macroscopic Fundamental Diagram Approach
    (2022-05) Hosseinzadeh, Fatemeh; Kattan, Lina; Demissie, Merkebe Getachew; Waters, Nigel Michael
    This thesis introduces fair route guidance (RG) control schemes in a model predictive control (MPC) framework. The modelling approach used is based on the macroscopic fundamental diagram (MFD), which relates aggregated traffic variables, such as vehicle accumulation and trip completion rate. Earlier MFD-based RG schemes focus on improving network efficiency while overlooking fairness and equity issues. As a result, the controllers force some drivers to take longer paths for their trip to minimize total network travel time; thus, creating inequity issues. This problem motivated this thesis to develop a new control strategy that simultaneously addresses fairness and efficiency in RG control models. This is done by introducing various fairness-centered concepts, such as proportional fairness and anticipatory RG control in an MPC framework for online control. The proportional fairness (PF) concept, which is rooted in economics and was successfully applied in wireless networks can address this issue by balancing the trade-off between network efficiency and fairness. This paper presents a two-level RG framework using MFD for a heterogeneous urban network divided into multiple pockets of congestion. The developed framework comprises an MPC-based RG optimization and an estimated route-choice model. Firstly, the optimized RG ratios are obtained from the optimization model with different objective functions including: proportional fairness of regional speed, path speed, and path travel time. These objective functions are examined and the results are compared. Then, to update the network traffic states, the drivers’ actual route choice is estimated based on the linear combination, including the driver routing responses through a logit route-choice model and the optimized routing ratios, which is determined by using the given compliance rate. Also, this thesis presents an MFD-based anticipative RG control approach by modelling a two-level optimization model in an online optimization framework and directly incorporates road users’ routing behaviour in the control model. The anticipatory RG controller is examined by replacing the basic objective function with the proportional fairness objective function. Based on the anticipatory control (AC) concept, incorporating user behaviour proactively as part of the control framework leads to a more optimum solution and more consistent routing schemes. Intensive sensitivity analysis is conducted under high and low-demand profiles and for different compliance rates and MFD parameters. Compared with the basic control model, the results show that the fairness control models were more successful in reducing the variances of region accumulations and speeds. The results indicate that the proportionally fair RG model based on path time improves fairness in an urban network by increasing homogeneity while also maintaining a high level of efficiency. Having more homogenised traffic by FC models was consistent for all examined routing compliance levels, even when the compliance rates dropped to as low as 30%. However, because of integrating drivers routing decision directly with AC control models, the total travel time (TTT) efficiency of AC models was more than FC models in cases where the compliance rate was less than 70%. In addition, when examining the performance of the routing guidance for the scenarios with heterogeneous MFD parameters, the proportionally fair RG models exhibited a more homogenous traffic network by reducing the variance of speed, compared to other routing models.
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    On Utilization of Commercial Electric Vehicles for Grid Services
    (2024-09-11) Karimi, Hossein; Zareipour, Hamidreza; Rosehart, William Daniel; Drew, Steve H; Nielsen, Jorgen S; Demissie, Merkebe Getachew; Kamwa, Innocent
    The penetration of passenger electric vehicles into the grid has seen remarkable growth over the past years. This transition is now extending into the commercial sector, where fleets are shifting from conventional to electric vehicles. The aggregated batteries of these electric fleets can provide a large amount of energy and power. When strategically deployed, these batteries can help fleet owners reduce electricity costs by providing energy during peak hours, facilitating load shifting, or generating revenue by renting battery capacity to third parties or the grid. Therefore, this thesis explores the potential of Electric Commercial Fleets (ECFs) for energy services and examines the benefits they can offer to both fleet owners and the grid. In the first contribution of this thesis, a mutlistage investment planning is developed for the transition from conventional fleet to ECF. This work first analyzes the revenue opportunities within the electricity markets in North America. Next, it assesses how these auxiliary services can influence the dynamics of fleet transitions, reduce the total cost of ownership for fleet owners, and accelerate the transition to electric fleets. Two additional contributions focus on the daily operational aspects of these fleets. In the first contribution of operation planning, a new time-index Vehicle Routing Problem (VRP) is developed that helps commercial and industrial entity to concurrently optimize their logistics and energy provision for their self-use. The advantage of this method is shown compared to the conventional VRP. For larger benchmarks, Column Generation and Branch-and-Price techniques is employed to decompose the large instances into smaller, more manageable problems for efficient solving. In the last contribution, the electric fleet is utilized as mobile battery storage to address planned outages across the city of Calgary. A robust optimization counterpart is further developed to deal with the uncertainties of logistics and electricity demand. The results illustrate that ECFs have the potential to outperform a single mobile battery system both financially and technically. This advantage stems from employing the sharing economy concept and the presence of a larger number of vehicles distributed across the city.
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    Public transit planning with consideration to equity: A Utility Modelling Approach
    (2023-04-14) Senasinghe, Asiri Prabhath; Kattan, Lina; Wirasinghe, Sumedha Chandana; Waters, Nigel Michael; Behjat, Laleh; Demissie, Merkebe Getachew; Bandara, Jayaweera M.S.J. Saman
    Public transit plays a key role in human life. An efficient and equitable transportation system enables access to services and amenities that are central to the lives of all individuals, such as employment, education, health services, and leisure. Transit frequency is one of the key attributes of public transit that makes an equitable and efficient system a reality, allowing necessary freedom for users. However, from an operator’s perspective, high frequency is expensive. So far, we have mainly explored cost-effective solutions in allocating frequencies. In contrast, it is extremely difficult to single out a universal approach in defining or measuring transportation inequities because such an exercise highly depends on many interacting factors including background, population, administration policies, epoch, procedures, and perspectives of inequality itself. Ideally, when scheduling, transit planners should consider a) egalitarianism, where individuals and groups are treated equally, b) social inclusion, where distribution of impacts between individuals and groups were determined by income or social class, and c) differences in mobility ability and individual needs. In addition, one should consider many factors such as, demographic factors (age, gender, employment, income-level), geographic factors, mode of transportation, and trip purpose. From a mathematical perspective, distributive equity is extremely complex due to the complexity of the decision parameters, and almost all of the studies that have explored this subject quantify the aftermath of the distribution of impacts rather than define ways to make the distribution equitable in the first place. In this study, we bridge the gap in the equitable transit planning literature by introducing two novel utility-based mathematical approaches: i) proportional fairness and ii) alpha-fairness, which integrate all (a) to (c) aspects of equity (listed in the previous paragraph) and users’ perspectives of frequency. The numerical analysis addresses several variations of the fair allocation problem from vertical and horizontal equity within a unified model, which rectifies the gap in accessibility, comfort, impact of transit fare, and travel time in achieving equity. In this thesis, we show that, with the right set of definitions and optimization tools, one can account for complex granular public transit user parameters, the associated equity issues, and inequalities and disparities in society to create a fair yet efficient public transit system.
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    Reflections on the 2023 Bonn UN Climate Change Conference: An Engineer's Perspective
    (2023-07-20) Demissie, Merkebe Getachew
    The reflective process of the 2023 Bonn Climate Change Conference involves examining my initial expectations, reflecting on the conference experience, and identifying key takeaways that significantly impacted my professional development and understanding of climate change. This exercise aims to deepen my knowledge of global developments in climate change mitigation, adaptation, and financing. The conference serves as a powerful platform for raising awareness and promoting change. Additionally, I hope to inspire greater participation in the engineering community, leveraging this conference to increase public awareness about climate change and the vital role engineering plays in addressing these challenges.
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    Travel 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 Getachew
    Extensive 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.