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The pace of changes in automating cars has sped up in the last few decades. Autonomous Vehicles (AVs) will dramatically change the future of transportation, and household-level decisions will play a large role in the AV market. However, no data is readily available on household travel behavior using AVs. This study introduces a framework to assess households’ adaptation to AV operations. We developed a mixed integer program, Household Activity Pattern Problem with AV (HAPPAV), to model traveler behavior under realistic conditions while using AVs. The model generates feasible activity patterns for household members under spatial and temporal constraints. The model is able to consider complete driverless operations, such as AV pick-up and drop-off, parking availability, empty trips, and carpooling. A decomposition method is developed to solve the NP-hard problem HAPPAV. The method includes two major stages; the first stage is to generate all feasible travel patterns for household members and the second stage finds the best AV route along with detailed travel patterns. We also use novel pruning rules to enhance the performance of the decomposition method. The model is applied on the California Statewide Travel Survey. The results indicate that 62% of households can perform their daily activities with only one AV in place of two or three regular vehicles. However, AV empty trips increase total VMT by 15%. The new method improves the average runtime and solution quality by 86% and 23%, respectively.
Yashar Khayati; Jee Eun Kang; Mark Karwan; Chase Murray. Household Activity Pattern Problem with Autonomous Vehicles. Networks and Spatial Economics 2021, 1 -29.
AMA StyleYashar Khayati, Jee Eun Kang, Mark Karwan, Chase Murray. Household Activity Pattern Problem with Autonomous Vehicles. Networks and Spatial Economics. 2021; ():1-29.
Chicago/Turabian StyleYashar Khayati; Jee Eun Kang; Mark Karwan; Chase Murray. 2021. "Household Activity Pattern Problem with Autonomous Vehicles." Networks and Spatial Economics , no. : 1-29.
The prospect of autonomous vehicles (AVs) offers the possibility that a household could reduce household owned vehicles to a single vehicle. At the same time, with AVs, a Shared Autonomous Vehicle (SAV) system will rise as a primary mode of serving travel demands. This study aims to model households’ activity/travel decisions using both household owned AVs as well as readily available SAVs to perform daily activities. We formulate the Household Activity Pattern Problem with Autonomous Vehicles and Ride Sourcing (HAPPAV-RS), as a mixed integer linear program. The model generates an optimal activity/travel patterns for household members under spatial and temporal constraints. The model can capture driverless operations such as AV pick-up and drop-off, parking availability, empty trips and ridesharing among household members, as well as the use of SAVs with the request waiting time. A decomposition method is used to solve the NP-hard problem HAPPAV-RS. scenarios on using AVs and SAVs with different travel mode availability, AV/SAV cost and SAV waiting time are designed to enable sensitivity analysis. Various travel metrics such as activity pattern feasibility, household’s total travel disutility, travel mode VMT and AV-SAV trip coverage are reported.
Yashar Khayati; Jee Eun Kang; Mark Karwan; Chase Murray. Household use of autonomous vehicles with ride sourcing. Transportation Research Part C: Emerging Technologies 2021, 125, 102998 .
AMA StyleYashar Khayati, Jee Eun Kang, Mark Karwan, Chase Murray. Household use of autonomous vehicles with ride sourcing. Transportation Research Part C: Emerging Technologies. 2021; 125 ():102998.
Chicago/Turabian StyleYashar Khayati; Jee Eun Kang; Mark Karwan; Chase Murray. 2021. "Household use of autonomous vehicles with ride sourcing." Transportation Research Part C: Emerging Technologies 125, no. : 102998.
Eliciting individual travelers’ Origin-Destination (OD) information is critical for enabling public transit system policy-makers and operators to serve travelers in a calculated way. Accurate estimation of route choice model parameters is also important, in that it can help assess or predict the service levels that such a system can be expected to achieve. The knowledge of both the OD links and route choice logic is especially in demand for emerging mobility services, where providers work to accommodate individualized services and also offer incentives to travelers for specific trips. We show that all this information can be distilled from a particular type of data – the Automated Fare Collection (AFC) system data – in a fast, low-cost way. This paper presents a two-step methodological framework to identify individual travelers’ true ODs (beyond stop-level ODs), as well as infer their travel preferences. The key to our work is the ability to identify and process the observations of travelers’ routing choices between the same ODs under different travel environment conditions. A presented specially-crafted case study validates the proposed method in application with a real-world AFC data of Seoul, Korea, confirming the method’s high inferential ability, under a basic route choice model.
LaiYun Wu; Jee Eun Kang; Younshik Chung; Alexander Nikolaev. Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data. Omega 2020, 101, 102260 .
AMA StyleLaiYun Wu, Jee Eun Kang, Younshik Chung, Alexander Nikolaev. Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data. Omega. 2020; 101 ():102260.
Chicago/Turabian StyleLaiYun Wu; Jee Eun Kang; Younshik Chung; Alexander Nikolaev. 2020. "Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data." Omega 101, no. : 102260.
We investigate a new form of car-sharing system that can be introduced in the market for autonomous vehicles called fractional ownership or co-ownership. Although dynamic ride sharing provides ad hoc shared mobility services without any long-term commitment, we consider co-ownership programs with which users can still “own” a car with committed usage and ownership. We assume that an autonomous vehicle is shared by a group of users, which is only accessible by the group. We use stable matching to help users find an appropriate group with which to share an autonomous vehicle and present a generalized stable matching model that allows flexible sizes of groups as well as various alternative objectives. We also present a heuristic algorithm to improve computational time owing to the combinatorial properties of the problem.
Anpeng Zhang; Jee Eun Kang; Changhyun Kwon. Generalized Stable User Matching for Autonomous Vehicle Co-Ownership Programs. Service Science 2020, 99, 1 -18.
AMA StyleAnpeng Zhang, Jee Eun Kang, Changhyun Kwon. Generalized Stable User Matching for Autonomous Vehicle Co-Ownership Programs. Service Science. 2020; 99 (99):1-18.
Chicago/Turabian StyleAnpeng Zhang; Jee Eun Kang; Changhyun Kwon. 2020. "Generalized Stable User Matching for Autonomous Vehicle Co-Ownership Programs." Service Science 99, no. 99: 1-18.
Multi-day activity-travel patterns help create potential vehicle usage profiles that contain vehicle operations and battery status under different scenarios with varying location-based charging opportunities, based on travel needs and charging availability/behaviors. Utilizing a multi-day data sampling method, analyses of scenarios are designed to provide insights on bounds of potential BEV market under different charging opportunities, including level 2 activity charging and level 3 trip charging. Single-day data results tend to overestimate travelers’ BEV feasibility assuming that multi-day sample data provides accurate estimations. Facility utilization can be improved without affecting travelers’ charging demand under correct pricing scheme for most cost-sensitive users. Smart grid charging strategy can greatly reduce the total number of operating chargers during the same time in a day, and BEV users’ charging behaviors have minor impact on this improvement. Our numerical results indicate that an appropriate number of chargers installed in shopping and leisure locations should be more profitable and have higher charger utilization rate since those chargers help cover BEV users’ trips.
Anpeng Zhang; Jee Eun Kang; Changhyun Kwon. Multi-day scenario analysis for battery electric vehicle feasibility assessment and charging infrastructure planning. Transportation Research Part C: Emerging Technologies 2020, 111, 439 -457.
AMA StyleAnpeng Zhang, Jee Eun Kang, Changhyun Kwon. Multi-day scenario analysis for battery electric vehicle feasibility assessment and charging infrastructure planning. Transportation Research Part C: Emerging Technologies. 2020; 111 ():439-457.
Chicago/Turabian StyleAnpeng Zhang; Jee Eun Kang; Changhyun Kwon. 2020. "Multi-day scenario analysis for battery electric vehicle feasibility assessment and charging infrastructure planning." Transportation Research Part C: Emerging Technologies 111, no. : 439-457.
Monitoring transit system “health” by extracting and tracking such quantities as travel time, transfer time, number of passengers, etc., is critical to the benefit of travelers, planners and operators within a transit system. Most of the data typically available to and useful for analysts are generated by tracking vehicles instead of individual passengers/travelers—these data are useful, albeit within certain limits. This paper presents methods for obtaining system-level transit information from a new type of data—that coming from an Automated Fare Collection (AFC) system,—which provides hour-to-hour, day-to-day transit information, such as the value and reliability in both travel time and traveler count, and the location of congested road clusters in a city. The AFC data of public transit system in Seoul, South Korea is used as an example to illustrate the proposed data extraction methods and analysis. This paper is structured and detailed so as to provide both methodological and practical guidance for researchers and data-handling analysts.
LaiYun Wu; Jee Eun Kang; Younshik Chung; Alexander Nikolaev. Monitoring Multimodal Travel Environment Using Automated Fare Collection Data: Data Processing and Reliability Analysis. Journal of Big Data Analytics in Transportation 2019, 1, 123 -146.
AMA StyleLaiYun Wu, Jee Eun Kang, Younshik Chung, Alexander Nikolaev. Monitoring Multimodal Travel Environment Using Automated Fare Collection Data: Data Processing and Reliability Analysis. Journal of Big Data Analytics in Transportation. 2019; 1 (2-3):123-146.
Chicago/Turabian StyleLaiYun Wu; Jee Eun Kang; Younshik Chung; Alexander Nikolaev. 2019. "Monitoring Multimodal Travel Environment Using Automated Fare Collection Data: Data Processing and Reliability Analysis." Journal of Big Data Analytics in Transportation 1, no. 2-3: 123-146.
This paper presents a new conceptual approach to improve the operational performance of public bike sharing systems using pricing schemes. Its methodological developments are accompanied by experimental analyses with bike demand data from Capital Bikeshare program of Washington, DC (USA). An optimized price vector determines the incentive levels that can persuade system customers to take bikes from, or park them at, neighboring stations so as to strategically minimize the number of imbalanced stations. This strategy intentionally makes some imbalanced stations even more imbalanced, creating hub stations. This reduces the need for trucks and dedicated staff to carry out inventory repositioning. For smaller networks, a bi-level optimization model with a single level reformulation is introduced to minimize the number of imbalanced stations optimally. The results are compared with a heuristic approach that adjusts route prices by segregating the stations into different categories based on their current inventory profile, projected future demand, and maximum and minimum inventory values calculated to fulfill certain desired service level requirements. We use a routing model for repositioning trucks to show that the proposed optimization model and the latter heuristic approach, called the iterative price adjustment scheme (IPAS), reduce the overall operating cost while partially or fully obviating the need for a manual repositioning operation.
Zulqarnain Haider; Alexander Nikolaev; Jee Eun Kang; Changhyun Kwon. Inventory rebalancing through pricing in public bike sharing systems. European Journal of Operational Research 2018, 270, 103 -117.
AMA StyleZulqarnain Haider, Alexander Nikolaev, Jee Eun Kang, Changhyun Kwon. Inventory rebalancing through pricing in public bike sharing systems. European Journal of Operational Research. 2018; 270 (1):103-117.
Chicago/Turabian StyleZulqarnain Haider; Alexander Nikolaev; Jee Eun Kang; Changhyun Kwon. 2018. "Inventory rebalancing through pricing in public bike sharing systems." European Journal of Operational Research 270, no. 1: 103-117.
Jiangtao Liu; Jee Eun Kang; Xuesong Zhou; Ram Pendyala. Network-oriented household activity pattern problem for system optimization. Transportation Research Part C: Emerging Technologies 2018, 94, 250 -269.
AMA StyleJiangtao Liu, Jee Eun Kang, Xuesong Zhou, Ram Pendyala. Network-oriented household activity pattern problem for system optimization. Transportation Research Part C: Emerging Technologies. 2018; 94 ():250-269.
Chicago/Turabian StyleJiangtao Liu; Jee Eun Kang; Xuesong Zhou; Ram Pendyala. 2018. "Network-oriented household activity pattern problem for system optimization." Transportation Research Part C: Emerging Technologies 94, no. : 250-269.
The key purpose of this paper is to demonstrate that optimization of evacuation warnings by time period and impacted zone is crucial for efficient evacuation of an area impacted by a hurricane. We assume that people behave in a manner consistent with the warnings they receive. By optimizing the issuance of hurricane evacuation warnings, one can control the number of evacuees at different time intervals to avoid congestion in the process of evacuation. The warning optimization model is applied to a case study of Hurricane Sandy using the study region of Brooklyn. We first develop a model for shelter assignment and then use this outcome to model hurricane evacuation warning optimization, which prescribes an evacuation plan that maximizes the number of evacuees. A significant technical contribution is the development of an iterative greedy heuristic procedure for the nonlinear formulation, which is shown to be optimal for the case of a single evacuation zone with a single evacuee type case, while it does not guarantee optimality for multiple zones under unusual circumstances. A significant applied contribution is the demonstration of an interface of the evacuation warning method with a public transportation scheme to facilitate evacuation of a car-less population. This heuristic we employ can be readily adapted to the case where response rate is a function of evacuation number in prior periods and other variable factors. This element is also explored in the context of our experiment.
Dian Sun; Jee Eun Kang; Rajan Batta; Yan Song. Optimization of Evacuation Warnings Prior to a Hurricane Disaster. Sustainability 2017, 9, 2152 .
AMA StyleDian Sun, Jee Eun Kang, Rajan Batta, Yan Song. Optimization of Evacuation Warnings Prior to a Hurricane Disaster. Sustainability. 2017; 9 (11):2152.
Chicago/Turabian StyleDian Sun; Jee Eun Kang; Rajan Batta; Yan Song. 2017. "Optimization of Evacuation Warnings Prior to a Hurricane Disaster." Sustainability 9, no. 11: 2152.