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Now showing 1 - 5 of 15
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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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    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.