Urban Mobility & Equity Center

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The Urban Mobility & Equity Center (UMEC) focuses on research to improve urban mobility of people and goods in an environmentally sustainable and equitable manner. Based at Morgan State University, UMEC includes the University of Maryland and Virginia Polytechnic Institute and State University (Virginia Tech).


Recent Submissions

Now showing 1 - 20 of 20
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    How Mobility and Accessibility Affect Crime Rates: Insights from Mobile Device Location Data
    (2021-03-30) Zhang, Lee; Yang, Mofeng; Zhao, Guangchen; Darzi, Aref; Ghader, Sepehr; Urban Mobility & Equity Center
    This research study investigates the possible correlations between mobility, accessibility, and crime rate. A rich mobile device location dataset including detailed anonymized location traces of the mobile devices observed in the City of Baltimore was combined with the police arrest records to study how mobility and accessibility affect neighborhood safety. The research team first processed and analyzed the mobile device location dataset to obtain measures of mobility and accessibility. These measures differed from the traditional measures in that they were obtained based on the empirically observed location data. The research team then built statistical and machine learning tools to model crime rates at the census-tract levels, using the calculated mobility and accessibility measures, land-use variables, and socioeconomic-related variables as the covariates. Subsequently, the team focused on the correlation of the crime rates with the mobility and accessibility variables. Results indicated that the mobility and accessibility measures can help improve the performance of crime rate prediction. Also, non-motorized travel might be positively related to burglary. The study seeks to inform decision-makers about the transportation-related issues contributing to the lack of safety and offer transportation solutions to crime-related problems, especially in the neighborhoods suffering from high crime rates.
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    E-Bikes’ Effect on Mode and Route Choice: A Case Study of Richmond, VA Bike Share
    (2021-03) Chavis, Celeste; Frias-Martinez, Vanessa; Urban Mobility & Equity Center
    The bicycle has become a legitimate transportation option in many cities due to its different benefits. Lower transportation costs, health improvement, and lower emission rates are some critical benefits of a bicycle ride. A large body of research exists on bicycle route choice and travel behavior. There is currently a lack of research on mode shift and route choice changes with the introduction of e-bikes. This study has presented a comprehensive analysis of the similarities and differences between a pedelec and regular bicycle use in Richmond City, Virginia, as well as an evaluation of how membership type and other user characteristics might influence bike share use. This study utilized GPS data for a docked bike-share system in Richmond, Virginia, from March 2019, when RVA Bike Share began converting the traditional bikes to e-bikes. To retrieve the data this study used Mapbox's Map Matching API, which snaps fuzzy, inaccurate GPS traces to actual segments in the road network, breaks the snapped roads into segments and queries each segment in Open Street Maps (OSM) to identify the type of road. This study did a comprehensive descriptive analysis, origin-destination trip analysis, and user cluster analysis with the retrieved data. The results have shown that pedelecs are generally associated with longer trip distances, shorter trip times, higher speeds and lower elevations. In April, about 25% of the fleet was pedelec bikes and by December, approximately 65%. The t-tests results showed that the mean number of trips made per bike available was significantly more (~3.2x) for pedelecs compared to bikes (p-value=0.004). The origin-destination analysis considered the business, mixed use, residential and other uses and observed that the plots show extremely similar trends with a large number of trips staying within either business or residential locations or mixed use. The roadway use analysis and mapping showed that pedelecs were used farther outside of the city than bikes. Additionally, pedelecs were frequently used in the downtown core where most RVA bike share stations are located. In terms of memberships, longer-term memberships (annual, monthly) were found to be associated with significantly higher use of pedelecs than shorter-term memberships, potentially pointing to a lack of knowledge on the part of those who use the system less frequently or to a preference for normal bicycles. Finally, the user cluster analysis identified six diverse types of behaviors that varied by geographical region (e.g., central Richmond vs. recreational areas), as well as by trip distance, trip duration, and bike type.
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    Equity in Accessibility to Opportunities: Insights, Measures, and Solutions based on Mobile Device Location Data
    (2021-03) Zhang, Lei; Shin, Hyeon-Shic; Ghader, Sepehr; Darzi, Aref; Zhao, Guangchen; Kabiri, Aliakbar; Urban Mobility & Equity Center
    This report summarizes the study of accessibility to opportunities among different population groups and neighborhoods in Baltimore City. The study is the first of its kind in utilizing observed multimodal mobile device location data from individual devices to systematically study accessibility to opportunities. Passively collected mobile device location data used in this study reveal day-to-day travel patterns of more than 25% of the U.S. population for an entire year across the nation. To showcase the application of this data, we selected the Baltimore city as our testbed. This new data source with very high sampling rates, combined with point of interest data and census data, allows us to analyze how residents in each neighborhood travel to work or seek their essential needs such as food and healthcare. The study introduces a data-driven accessibility measure based on the observed location data, which can also be calculated using individual-level outputs of a typical activity-based model. Research findings directly identify accessibility gaps among neighborhoods. In addition to the above, accessibility and equity measures from mobile device location data are compared with traditional measures, and the comparison results are discussed. Furthermore, this study draws on information from the data-driven method to capture the differences in accessibility among different income groups.
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    How Mobility and Accessibility Affect Crime Rates: Insights from Mobile Device Location Data
    (2021-03) Zhang, Lei; Yang, Mofeng; Zhao, Guangchen; Darzi, Aref; Ghader, Sepehr; Urban Mobility & Equity Center
    This research study investigates the possible correlations between mobility, accessibility, and crime rate. A rich mobile device location dataset including detailed anonymized location traces of the mobile devices observed in the City of Baltimore was combined with the police arrest records to study how mobility and accessibility affect neighborhood safety. The research team first processed and analyzed the mobile device location dataset to obtain measures of mobility and accessibility. These measures differed from the traditional measures in that they were obtained based on the empirically observed location data. The research team then built statistical and machine learning tools to model crime rates at the census-tract levels, using the calculated mobility and accessibility measures, land-use variables, and socioeconomic-related variables as the covariates. Subsequently, the team focused on the correlation of the crime rates with the mobility and accessibility variables. Results indicated that the mobility and accessibility measures can help improve the performance of crime rate prediction. Also, non-motorized travel might be positively related to burglary. The study seeks to inform decision-makers about the transportation-related issues contributing to the lack of safety and offer transportation solutions to crime-related problems, especially in the neighborhoods suffering from high crime rates.
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    Adoption and Diffusion of Electric Vehicles in Maryland
    (2021-06) Cirillo, Cinzia; Bas, Javier; Urban Mobility & Equity Center
    Among the many approaches toward fuel economy, the adoption of electric vehicles (EVs) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users’ inclinations toward EVs. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g., only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well differentiated groups, unveiling the importance that the profiling of a potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption.
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    Investigating the Effect of Connected Vehicles (CV) Route Guidance on Mobility and Equity
    (2022-02-25) Jeihani, Mansoureh; Ansariyar, Alireza; Sadeghvaziri, Eazaz; Ardeshiri, Anam; Kabir, Md Muhib; Haghani, Ali; Jones, Anita; Urban Mobility & Equity Center/National Transportation Center
    Traffic congestion is a serious and increasing national problem, especially for urban commuters. Providing accurate real-time traffic information is a key tool to reduce congestion. Recent studies have shown that connected vehicles (CVs) can help improve traffic mobility and safety while saving energy and reducing emissions. The research initially evaluates the gradual deployment of CVs and their effect on mobility, energy consumption, and the amount of pollutants. Then, our research investigates the CV guidance system as an emerging form of dynamic route guidance. This research develops and calibrates a microscopic traffic simulation model to replicate the fairly realistic behavior of such vehicles in the traffic simulation environment. Unlike the majority of prior studies that used hypothetical study areas with simple networks, this study develops a real-world medium urban road network. Different penetration rates of CVs (0%-100%) are developed, and the system-wide effects of CV equipped vehicles with route guidance features on mobility and equity are analyzed. The results showed that as the market penetration rate (MPR) of CVs increases, traffic parameters (e.g., total delay time), total emissions, and average travel time of re-routing paths decreases. In order to find the effects of new traffic reduction policies for mass public transportation systems, dynamic CV bus lanes were suggested. The results showed that increasing the service time of a dynamic CV bus lane may improve average travel time for CV buses, but it negatively affects the average travel time of non-CV and CV cars. Finally, a network-wide average travel time analysis is proposed. Based on the proposed methodology, 85% MPR was determined as a critical breakpoint of the network-wide weighted average travel time chart. The results of network-wide equity analysis highlighted that, as the MPR of CVs increases, the percentage of critical breakpoint decreases, and that point shifts to the left of the chart
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    Multi-depot and Multi-school Bus scheduling Problem with School Bell Time Optimization
    (2022-04-12) He, Qinglian; National Transportation Center Urban Mobility & Equity Center Morgan State University 1700 E. Cold Spring Lane CBEIS 327 Baltimore, MD 21251 (443) 955-2729
    Abstract Public school systems are responsible for transporting students to and from schools safely and promptly. For a multi-school system, given all the bus trips of schools, the school bus scheduling problem aims at developing an efficient schedule of operation for buses to serve all the trips at minimum operation costs while satisfying some necessary constraints. As for school buses, they usually operate from multiple depots and are required to return to the same depot as they started from. However, most existing studies have concentrated on the single-depot school bus scheduling problem, which assumes that all the buses start from the same depot. This research studies the multi-depot and multi-school bus scheduling problem with school bell time optimization (MDBSPBO) with the goal of minimizing the total number of buses and the total deadhead duration. Spreading bell times, which change the bell times within a reasonable time window, makes more trips become compatible and could reduce the total number of buses. We will develop appropriate models and solution algorithms for this very important real-world problem. We will use real-world data supplied by one of the public school systems in the state of Maryland for testing and evaluation of the model.
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    EQUITABLE COMPLETE STREETS: Data and Methods for Optimal Design Implementation
    (2022-04-12) Cirillo, Cinzia; Jehiani, Mansoureh; Schonfeld, Paul; National Transportation Center Urban Mobility & Equity Center Morgan State University 1700 E. Cold Spring Lane CBEIS 327 Baltimore, MD 21251 (443) 955-2729; National Transportation Center
    The Complete Streets concept references roads designed to accommodate: (1) diverse modes, including walking, cycling, public transit, and automobile; (2) different users, e.g. affluent and low-income individuals, people with disabilities, and senior citizens; (3) and a mix of land uses such as office, retail, businesses, and residential to ensure streets are safe, balanced and inclusively support diverse economic, cultural and environmental uses. Today most of our streets are poorly designed and do not offer safe places to walk, bike, or take public transportation. Such streets are particularly dangerous for disadvantaged segments of the population, including people of color, older adults, children, and those living in low-income communities. Successful Complete Streets projects prioritize multi-modal transport systems and have been demonstrated to be effective in fostering more livable communities, increasing equity, and improving public health. This project analyzes different components of Complete Streets' design and uses with the goal of creating fast, low-cost, and high-impact (transportation) changes in our communities. In recent years, “complete streets” has been an emerging concept in North American transportation planning and design. To be considered a “complete street”, a road should be designed to be safe for users of all traffic modes. This report presents three studies: safety evaluation on the complete streets by simulating different modes, quantifying the benefits of complete streets in terms of equity and improved access across different segments of the population (especially low-income), and road space allocation on the complete streets.
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    Shared Bus-Bike Lane Safety Analysis: Assessing Multimodal Access and Conflicts
    (Morgan State University 1700 E. Cold Spring Lane Baltimore, MD 21251, 2022-07-01) Chavis, Celeste. Ph.D.; Bhuyan, Istiak, Ph.D.; Cirillo, Cinzia, Ph.D.
    Dedicated bus facilities are being installed across the country with many jurisdictions allowing cyclists to use these facilities. Known as shared-bus bike lanes (SBBLs), these facilities are built with two, often opposing, goals in mind: (1) provide a high-speed travel lane for buses and (2) provide a safe travel lane for cyclists. Using video observation and survey data, the aim of this study is to analyze cyclist safety on SBBLs as a function of geometric configuration, bus frequency, and level of service. The safety of cyclins on SBBLs will be compared with separated bike facilities with adjacent bus routes.
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    Fare Free Public Transportation: A full-scale, real-world experiment in Alexandria (VA)
    (2023-08) Cirillo, Cinzia; Tabrizi, Asal Mehdi; Rakha, Hesham; Du, Jianhe; Urban Mobility & Equity Center
    The Fare Free Public Transportation (FFPT) concept is a common part of the agenda among transit agencies and state and federal policy makers. The subject is particularly important in the post-pandemic period, as transit use is slowly recovering but has not yet reached pre-pandemic ridership and market share. FFPT has been implemented in Europe and to a certain degree in the USA; however, there are very few studies that have effectively collected data and evaluated the consequences with respect to its implementation. This study monitored a full-scale, real-world FFPT plan implemented in Alexandria, VA in the Fall of 2021, separating respondents into treatment and control groups. Descriptive statistics indicated minimal disparity between the treatment and control groups across most socio-demographic variables. Notably, residents of Alexandria exhibit a higher propensity to use buses compared to the control group, both prior to and post-policy implementation. Regarding awareness of the policy, a majority of respondents were uninformed, while the policy's impact is more pronounced among those who were aware. Around 32% of respondents increased their bus usage following FFPT implementation, with approximately 80% of this subset utilizing buses more frequently than before. This policy evaluation is relevant not only to Alexandria, but to many stakeholders across the country that are considering similar policies in other cities.
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    A Knowledge-Based Expert System for Pedestrian Safety Improvement at Intersections
    (2023-06-01) Chang, Gang-Len; Chan, Yam Ting; Cheng, Yao; Urban Mobility & Equity Center
    In response to the rising concerns about intersection safety across the United States, traffic administrators have developed various techniques to create more effective and targeted improvement projects. Among them, Knowledge-Based Expert Systems (KBESs) demonstrate the unique advantage of having low requirements for users' experience and efficient decision-making. Recognizing that existing KBESs often lack comprehensive analysis of the critical factors contributing to pedestrian-involved crashes and the capability to optimize countermeasure selection, this study proposes an enhanced KBES to assist the traffic community in efficiently generating a set of optimal cost-benefit countermeasures to address pedestrian safety risks at intersections. In the proposed KBES, the carefully designed knowledge acquisition process fills two knowledge bases: one containing well-evidenced cause-effect relationships between contributing factors and corresponding Safety Related Intersection Characteristics (SRICs), and the other storing various attributes of a comprehensive list of countermeasures. The first developed inference engine is capable of identifying the contributing factors at an intersection and innovatively quantifying the impact of each of them based on the user input of SRICs. The second inference engine optimizes the countermeasure selection to maximize the expected effectiveness in accurately targeting the impact of those contributing factors while accounting for both budget constraints and users' defined priorities among the countermeasures' attributes. The results of the performance evaluation indicate that the proposed KBES is effective in analyzing contributing factors and recommending countermeasures and can serve as an efficient tool for traffic engineers to develop safety improvement projects at intersections
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    (2020-02) Cirillo, Cinzia; Nejad, Mohammad; Erdogan, Sevgi; Urban Mobility & Equity Center; USDOT University Transportation Centers Program
    Natural or man-made hazards that require evacuation put already vulnerable populations in a more precarious situation. When plans and decisions about evacuation are made, access to a private car is typically assumed, and differences in income levels across a community are rarely taken into account. The result is that carless members of a community can find themselves stranded. Low-income carless residents need alternative transportation means to reach shelters in case of an emergency. Thus, evacuation plans, decisions, and models need necessary information that identifies and locates these populations. In this study, data from the American Community Survey, U.S. Census, Internal Revenue Service, and the National Household Travel Survey are used to generate a synthetic population for Anne Arundel County, Maryland, using the copula concept. Geographic locations of low-income residents are identified within each subarea of the county (census tract) and their car ownership is estimated with a binomial logit model. The developed population synthesis method allows officials to have a more accurate account of populations for emergency planning and identify locations of shelters and triage points as well as planning carless transportation services.
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    Investigating the Impact of Distracted Driving among Different Socio-Demographic Groups
    (2019-12) Jeihani, Mansoureh; Ahangari, Samira; Hassan Pour, Arsalan; Khadem, Nashid; Banerjee, Snehanshu; Urban Mobility & Equity Center
    Previous studies examined the detrimental impact of distracted driving on safety; however, the effect of different types of distraction accompanied by different road classes has not been investigated. This study used a high-fidelity driving simulator and an eye-tracking system to examine the driving behavior of young participants while engaged in various in-vehicle distractions - no cell phone, handsfree call, hand-held call, voice commands text, text, taking on or off clothing, and eating or drinking - on different road classes: rural collector, freeway, urban arterial, and local road in a school zone; and with an out-of-vehicle billboard distraction. Some 92 participants drove a simulated network in the Baltimore Metropolitan Area with seven scenarios (one base scenario without any distraction and six different types of distractions). Participants also completed questionnaires documenting demographics and driving behavior before and after the driving simulator experience. The descriptive and statistical analysis of in-vehicle distractions revealed how they negatively impact safety: Participants exhibited greater fluctuations in speed, changed lanes significantly more times, and deviated from the center of the road when they were distracted while driving. The results indicated that drivers reduced their speed by up to 33% while distracted with hands free/voice command cell phone usage, which is inconsistent with the current cell phone usage policies in most states. The highest speed reduction happened on the local road when taking on/off clothing (50%), voice command texting (33%), and texting (29%). Visibility and gender significantly affected gaze fixation duration on billboards. Female participants had lower gaze fixation duration than their male counterparts on billboards, while males had less gaze fixation duration on the phone than female. The billboard with a lower cognitive load had less gaze fixation duration than the one with a higher cognitive load.
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    Innovative Methods for Delivering Fresh Foods to Underserved Populations
    (2019-12) Shin, Hyeon-Shic; Schonfeld, Paul; Lee, Young-Jae; Urban Mobility & Equity Center
    Limited access to fresh food sources--ones within reasonable distances with reliable, affordable transportation--has become a public health concern. The negative associations between a lack of fresh food consumption and health are well known. Because certain demographic groups are disproportionately affected by the absence of stores selling healthy and affordable food, equity issues result. Many inner-city residents are left in neighborhoods devoid of such stores, and every day they are forced to trade off increased costs against healthy food consumption and health. This study aimed to develop a cost-effective last-mile fresh food delivery system to households in food deserts, which could help improve fresh food accessibility. Six alternative delivery modes--conventional trucks, e-bikes, shared-ride transit, parcel lockers, pop-up stores, and independently contracted drivers--were identified and optimized by employing Traveling Salesman Problem. Then we compared the results with the system's total costs. Sensitive analyses were conducted in terms of the time of delivery, zone size, user's value of time waiting for goods, the optimal number of lockers, costs associated with combined deliveries at lockers as well as customer addresses, and a second delivery attempt. Building on optimized modes, GIS network analyses were performed for randomly selected household locations in parts of poverty-prone West Baltimore. Numerical results showed that deliveries by trucks are the most cost-effective alternative, while the third-party deliveries ranked second. The two most expensive alternatives were shared-ride service and e-bike deliveries, based on the estimated costs of providing them.
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    Optimized Development Of Urban Transportation Networks
    (2019-05-30) Schonfeld, Paul; Urban Mobility & Equity Center
    This report presents improved methods for planning and scheduling interrelated improvements in transportation networks. Due to the complexity of the relevant evaluation functions, which cannot be optimized with classical calculus techniques, the proposed methods rely on customized genetic algorithms for optimizing the selection, sequencing and scheduling of the interrelated alternatives. Three applications to urban transportation networks are presented in journal papers which are included in appendices. The papers demonstrate the applicability of the proposed methods to urban road networks, to intersections in urban road networks and to the development of urban rail transit networks.
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    Developing and Testing an ECO-Cooperative Adaptive Cruise Control System for Buses
    (2020-03-31) Rakha, Hesham; Chen, Hao; Jeihani, Mansoureh; Ahangari, Samira; Urban Mobility & Equity Center
    Studies over the past decade have shown that eco-driving systems which provide speed advisories to drivers/vehicles using data received via vehicle-to-infrastructure and vehicle-to-vehicle communications can help improve traffic mobility and reduce vehicle energy and emission levels. This study extends the Eco-Cooperative Adaptive Cruise Control (Eco-CACC) system previously developed for light duty vehicles to heavy duty vehicles (diesel and hybrid electric buses). First, the energy consumption models for diesel and hybrid buses are discussed and the field data collected by Blacksburg Transit are used to calibrate bus models. Thereafter, the bus Eco-CACC system is developed by incorporating the vehicle dynamic model and energy consumption model for buses. The developed Eco-CACC system has manual and automated modes to control buses. The manual Eco-CACC mode was tested by participants using driving simulators at Morgan State University under various scenarios that included different types of information. In addition, the automated bus Eco-CACC system was tested using the INTEGRATION microscopic simulation software to quantify the system-wide impacts of the proposed system under various traffic demand and vehicle types. The test results demonstrated that the proposed system could improve transit operations by reducing delay and helping transit agencies save on energy costs, resulting in an improved transit level of service, increased ridership, and improved traffic mobility.
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    Evaluating Equity Issues for Managed Lanes: Methods for Analysis and Empirical Results
    (2019-03) Cirillo, Cinzia; Bas Vicente, Javier; Urban Mobility & Equity Center
    Transportation planning decisions can have significant and diverse equity impacts (Litman, 2002). In particular, congestion and road pricing have raised equity concerns. Notably, the toll imposed on Managed Lanes on US highways affects drivers’ income. This is especially true for low-earning individuals, who devote a large portion of their available budget to transportation. Therefore, any policy or project assessment should take into consideration the so-called Income Effect. This concept refers to the fact that the impact of a change in driving cost – for instance, a toll increase – is not constant for all individuals but depends on their own income level. Unfortunately, the two measures most commonly used in project evaluation practice, Rule of a Half (RoH) and Log-sum (LS), rely on the assumption of absence of Income Effect. Since microeconomic theory does not support these grounds, not to account for income effect in policy evaluation may produce inaccurate results. Applying a policy for which the economic impact is not well-assessed may lead to severe equity issues. This project proposes a methodology that accounts for income effect in the appraisal of Managed Lanes and calculates the errors due to the use of approximated methods. In particular, the analysis is based on three pillars: i) the use of real data, ii) the use of more realistic assumptions about drivers’ behavior, considering different income levels and correlations between the alternatives, and iii) comparison of the LS and RoH and LS to the Compensating Variation (CV), the true benefit measure derived from microeconomic theory. These improvements provide a refined tool for the appraisal of the social, economic and equity aspects of transportation policy in the context of Managed Lanes. The tool will benefit private entities involved in road pricing projects, and transportation public agencies in need of ameliorating their evaluation of equity issues.
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    Dynamic (Time Dependent) Green Vehicle Routing Problem
    (2019-03-15) Haghani, Ali; Amoli, Golnush Masghati; Pternea, Moschoula; Department of Civil and Environmental Engineering, University of Maryland; University Transportation Centers Program
    This research summarizes recent studies on two versions of the Vehicle Routing Problem, i.e., the time-dependent vehicle routing problem (TD-VRP) and the green vehicle routing problem (G-VRP), for which a time-dependent version is also developed. A new formulation of TD-VRP is proposed that can deal with the time-dependent vehicle routing problem with dynamic demand information and provide the minimum cost routing plan. We also introduce a special case of G-VRP with a mixed fleet of heterogeneous electric and internal combustion engine commercial vehicles. Two different formulations are proposed to solve two different variants of the problem. A constructive heuristic is defined to generate initial feasible solution to the problem. The initial solution is further improved by deconstructing a large part of it and then, rebuilding it with the constructive heuristic. This algorithm is preferred over the local search algorithms as it provides better solutions due to the diversification effect embedded in it by deconstructing a large part of the solution. The results of the implementation of the proposed modes in a number of test problems and a large case study are also presented.
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    Sustainable Design of Concrete Bus Pads to Improve Mobility in Baltimore City
    (2019-03-02) Aslan, Kadir; Shokouhian, Mehdi; Civil Engineering; Urban Mobility & Equity Center
    Public transit, particularly buses in Baltimore City, plays a vital role in sustainable transportation in the United States as well as providing mobility to those without cars. Bus pads are usually constructed in the street, adjacent to a bus zone, to accommodate the weight of a bus. Bus pads are highly durable areas of the roadway surface at bus stops, usually made of concrete, addressing the common issue of asphalt distortion at bus stops. These concrete slabs bear the burden of the daily stream of buses better than asphalt. The major problem with the asphalt bus pads is shifting asphalt creating waves or ripples under buses’ weight, and when asphalt shifts, it cracks and can create potholes. Roadway pavements need to be strong enough to accommodate repetitive bus axle loads. Exact pavement designs will depend on site specific soil conditions. Areas where buses start, stop, and turn will be of particular concern for pavement design. Concrete pavement is desirable in these areas to avoid the failure problems that are experienced with asphalt. Concrete bus pads should be constructed based on the bus service frequency and type of transit vehicle used. However, if the concrete bus pad is not properly designed, it will encounter different problems with serviceability and strength of the slab. During a case study in Baltimore City that was used to collect preliminary data for the proposed research, it was observed that most of the concrete bus pads require more than regular routine maintenance due to surface cracks and local failure, resulting in major replacement costs for Baltimore City. Lack of appropriate load identification and definition of critical load scenarios for the appropriate design of the concrete bus pad were noted as shortcomings in addition to the design assumption of uniform distribution of soil pressure under the concrete slab, which was not the case noted in the field. This research carried out a field study and extracted two concrete strips in longitudinal and transverse axis from a bus pad in Baltimore. The concrete strips were tested at the Structures Laboratory of Morgan State University, under a four-point bending produced by two concentrated monotonic loads. The load and deflection were measured using precise instruments including LVDTs and load cells to investigate the concrete strips’ performances under the applied load until failure. All load cases and combinations were identified and determined based on possible loading scenarios. A numerical model was developed and soil-structure interaction was studied using the Winkler method. The maximum design forces and moments were extracted from the FE model, which considers the effect of moving loads on a two-way slab as well as the temperature. This research evaluated the load-bearing capacity of the current design of Baltimore bus pads and compared it to the tested strips as well as the required bending capacity of FE models. Results show that both design and construction of bus pads in Baltimore need to be modified. In conclusion, design and construction recommendations were proposed to enhance bus pads’ life span in Baltimore City to address the current issues and reduce maintenance costs.
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    Optimal Automated Demand Responsive Feeder Transit Operation and Its Impact
    (2018-09) Lee, Young-Jae; Nickkar, Amirezza; Urban Mobility & Equity Center; University Transportation Centers Program
    Although demand responsive feeder bus operation is possible with human-driven vehicles, it has not been very popular and mostly available as a special service because of the high operating costs due to the intensive labor costs as well as advanced real-time information technology and complicated operation. However, once automated vehicles become available, small-sized flexible door-to-door feeder bus operation will become more realistic, thanks to recent technological advances and business innovations by the transportation network companies (TNCs). So, preparing for the automated flexible feeder service is necessary to catch the rapid improvement of automated vehicle technology. Therefore, this research developed an algorithm for the optimal flexible feeder bus routing, which considers relocation of buses for multi-stations and multi-trains, using a simulated annealing (SA) algorithm for future automated vehicle operation. An example was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled relocating the buses when the optimal bus routings were not feasible with the available buses at certain stations. Furthermore, the developed algorithm limited the maximum Degree of Circuity for each passenger while minimizing total cost, including total vehicle operating costs and total passenger in-vehicle travel time costs. Unlike fixed route mass transit, small vehicle demand responsive service uses flexible routing, which means lower unit operating costs not only decrease total operating costs and total costs but also can affect routing and impact network characteristics. In the second part of this research, optimal flexible demand responsive feeder transit networks were generated with various unit transit operating costs using the developed routing optimization algorithm. Then network characteristics of those feeder networks were examined and compared. The results showed that when unit operating costs decline, total operating costs and total costs obviously decline. Furthermore, when unit operating costs decline, the average passenger travel distance and total passenger travel costs decline while the ratio of total operating costs per unit operating costs increases. That means if unit operating costs decrease, the portion of passenger travel costs in total costs increases, and the optimization process tends to reduce passenger costs more while reducing total costs. Assuming that automation of the vehicles reduces the operating costs, it will reduce total operating costs, total costs and total passenger travel costs as well.