MSU Faculty and Staff Collection

Permanent URI for this collectionhttp://hdl.handle.net/11603/7806

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Developing and Testing an Advanced Hybrid Electric Vehicle Co-Cooperative Adaptive Cruise Control System at Multiple Signalized Intersections
(2020-10) Chen, Hao; Rakha, Hesham; Jeihani, Mansoureh; Urban Mobility & Equity Center
This research develops an advanced Eco-Cooperative Adaptive Cruise Control System (Eco-CACC) for hybrid electric vehicles (HEVs) to pass signalized intersections with energy-optimized speed profiles, with the consideration of impacts by multiple signalized intersections. The research extends the Eco-CACC at signalized intersections (Eco-CACC-I) system previously developed by the research team for conventional internal combustion engine (ICE) vehicles to HEVs. In the proposed system, a simple HEV energy model is used to compute the instantaneous energy consumption level for HEVs. In addition, a vehicle dynamics model is used to capture the relationship between speed, acceleration level, and tractive/resistance forces on vehicles. The constraints of energy model and vehicle dynamics are used to develop two HEV Eco-CACC-I controllers for single-intersection and multiple-intersection, respectively. The developed HEV Eco-CACC-I controllers include two modes: automated and manual, for vehicles with or without an automated control system. The automated mode was implemented into the microscopic traffic simulation software so that connected and automated vehicles (CAVs) can directly follow the energy-optimized speed profile. Simulation tests using the INTEGRATION software validated the performances of the proposed controllers under the impact of signal timing, speed limit, and road grade. The simulation tests also demonstrated the improved benefits of using the proposed HEV Eco-CACC-I controllers in a traffic network with multiple intersections. Lastly, the manual model of the proposed HEV Eco-CACC controller was implemented in a driving simulator at Morgan State University so that drivers in connected vehicles (non-automated driving) can follow the recommended speed advisories. The data collected by the driving simulator with 48 participants demonstrated that the speed advisories calculated by the proposed controller can help drivers drive smoothly and save fuel while passing signalized intersections.
Developing and Testing an Advanced Hybrid Electric Vehicle Eco-Cooperative Adaptive Cruise Control System at Multiple Signalized Intersections
(2020-10) Chen, Hao; Rakha, Hesham; Jeihani, Mansoureh; Ahangari, Samira; Urban Mobi
This research develops an advanced Eco-Cooperative Adaptive Cruise Control System (Eco-CACC) for hybrid electric vehicles (HEVs) to pass signalized intersections with energy-optimized speed profiles, with the consideration of impacts by multiple signalized intersections. The research extends the Eco-CACC at signalized intersections (Eco-CACC-I) system previously developed by the research team for conventional internal combustion engine (ICE) vehicles to HEVs. In the proposed system, a simple HEV energy model is used to compute the instantaneous energy consumption level for HEVs. In addition, a vehicle dynamics model is used to capture the relationship between speed, acceleration level, and tractive/resistance forces on vehicles. The constraints of energy model and vehicle dynamics are used to develop two HEV Eco-CACC-I controllers for single-intersection and multiple-intersection, respectively. The developed HEV Eco-CACC-I controllers include two modes: automated and manual, for vehicles with or without an automated control system. The automated mode was implemented into the microscopic traffic simulation software so that connected and automated vehicles (CAVs) can directly follow the energy-optimized speed profile. Simulation tests using the INTEGRATION software validated the performances of the proposed controllers under the impact of signal timing, speed limit, and road grade. The simulation tests also demonstrated the improved benefits of using the proposed HEV Eco-CACC-I controllers in a traffic network with multiple intersections. Lastly, the manual model of the proposed HEV Eco-CACC controller was implemented in a driving simulator at Morgan State University so that drivers in connected vehicles (non-automated driving) can follow the recommended speed advisories. The data collected by the driving simulator with 48 participants demonstrated that the speed advisories calculated by the proposed controller can help drivers drive smoothly and save fuel while passing signalized intersections.
Optimized Development of Urban Transportation Networks 2.0
(2020-12) Schonfeld, Paul; Urban Mobility & Equity Center
This report presents as series of eight papers on methods for planning, designing, and scheduling the implementation of improvements in urban transportation systems. Five of the papers (1 - 4 and 6) focus on methods for evaluating, sequencing and scheduling interrelated improvements in transportation networks while the others present methods for designing flexible route services (5 - 7) and improving the reliability of rail transit networks (8). Due to the complexity of the relevant functions for evaluating interrelated network improvements, 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. Applications to urban transportation networks are presented in papers for journals, which are included in appendices. The papers demonstrate the applicability of the developed methods to urban road networks, intersections in urban road networks, urban rail transit networks and flexible-route transportation systems.
CSRC Oral History: Sogorea Te’ Land Trust Interviews
(2020-08-05) Brown, Nazshonnii; Morales, Harold; Wheeler, Kayla; Department of Philosophy and Religious Studies; Center for the Study of Religion and the City (CSRC)
The Sogorea Te’ Land Trust is an urban Indigenous women-led land trust based in the San Francisco Bay Area that returns Indigenous land to Indigenous people. It was founded in 2012 with the goals of returning traditionally Chochenyo and Karkin lands in the San Francisco Bay Area to Indigenous stewardship and cultivating more active, reciprocal relationships with the land. Through the practices of rematriation, cultural revitalization, and land restoration, Sogorea Te’ calls on native and non-native peoples to heal and transform the legacies of colonization, genocide, and patriarchy and to do the work our ancestors and future generations are calling us to do. The CSRC grant will be used to expand food production and distribution for members of urban Indigenous communities who have been affected by COVID-19.
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.
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.
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.
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.
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.
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
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.
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.
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.
Quantum Readiness in Healthcare and Public Health: Building a Quantum Literate Workforce
(2024-02-29) VanGeest, Johnathan B; Fogarty, Kieran J; Hervey, William G; Hanson, Robert A; Nair, Suresh; Akers, Timothy A
Quantum technologies, including quantum computing, cryptography, and sensing, among others, are set to revolutionize sectors ranging from materials science to drug discovery. Despite their significant potential, the implications for public health have been largely overlooked, highlighting a critical gap in recognition and preparation. This oversight necessitates immediate action, as public health remains largely unaware of quantum technologies as a tool for advancement. The application of quantum principles to epidemiology and health informatics, termed quantum health epidemiology and quantum health informatics, has the potential to radically transform disease surveillance, prediction, modeling, and analysis of health data. However, there is a notable lack of quantum expertise within the public health workforce and educational pipelines. This gap underscores the urgent need for the development of quantum literacy among public health practitioners, leaders, and students to leverage emerging opportunities while addressing risks and ethical considerations. Innovative teaching methods, such as interactive simulations, games, visual models, and other tailored platforms, offer viable solutions for bridging knowledge gaps without the need for advanced physics or mathematics. However, the opportunity to adapt is fleeting as the quantum era in healthcare looms near. It is imperative that public health urgently focuses on updating its educational approaches, workforce strategies, data governance, and organizational culture to proactively meet the challenges of quantum disruption thereby becoming quantum ready.
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.
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
E3: EVALUATING EQUITY IN EVACUATION: A PRACTICAL TOOL AND A CASE STUDY
(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.
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.
Developing an Eco-Cooperative Adaptive Cruise Control System for Electric Vehicles
(2020-03) Chen, Hao; Rakha, Hesham; Bas Vicente, Javier; Cirillo, Cinzia; Zofio, Jose; Urban Mobility & Equity Center
This study develops an Eco-Corporative Adaptive Cruise Control system (Eco-CACC) for battery electric vehicles (BEVs) in the vicinity of signalized intersections and investigates the network-level benefits of this system. The BEV Eco-CACC algorithms provide real-time energy-efficient speeds to connected automated EVs to optimize their travel through signalized intersections using Signal Phasing and Timing (SPaT) information received from traffic signal controllers and surrounding traffic information received from in-vehicle sensors. First, a basic BEV Eco-CACC algorithm was developed for a single intersection. After, an advanced algorithm called BEV Eco-CACC MS was developed with the consideration of impacts from queues and multiple intersections. The developed BEV Eco-CACC algorithms were implemented and tested using the INTEGRATION microscopic simulation software, considering different levels of market penetration rates, traffic conditions, signal timings, road grades, and vehicle types. The test results indicate that the energy-optimum solution for BEVs is different from that for internal combustion engine vehicles (ICEVs), thus demonstrating the need for vehicle-tailored optimum trajectories. The simulation tests demonstrate the BEV Eco-CACC MS produces up to 11% energy savings to pass multiple intersections. Lastly, the study conducts a stated choice experiment to unveil the inclination of drivers towards the Eco-CACC system and to calculate its potential market share. The results indicate that the Eco-CACC system can be very successful and that the overall attitude of individuals in favor of adopting of the system is capable of overturning the lack of private return on investment.
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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