Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making

Emergency vehicles (EVs) play a crucial role in providing timely help for the general public in saving lives and avoiding property loss. However, very few efforts have been made for EV prioritization on normal road segments, such as the road section between intersections or highways between ramps. In this paper, we propose an EV lane pre-clearing strategy to prioritize EVs on such roads through cooperative driving with surrounding connected vehicles (CVs). The cooperative driving problem is formulated as a mixed-integer nonlinear programming (MINP) problem aiming at (i) guaranteeing the desired speed of EVs, and (ii) minimizing the disturbances on CVs. To tackle this NP-hard MINP problem, we formulate the model in a bi-level optimization manner to address these two objectives, respectively. In the lower-level problem, CVs in front of the emergency vehicle will be divided into several blocks. For each block, we developed an EV sorting algorithm to design optimal merging trajectories for CVs. With resultant sorting trajectories, a constrained optimization problem is solved in the upper-level to determine the initiation time/distance to conduct the sorting trajectories. Case studies show that with the proposed algorithm, emergency vehicles are able to drive at a desired speed while minimizing disturbances on normal traffic flows. We further reveal a linear relationship between the optimal solution and road density, which could help to improve EV routing decision makings when high-resolution data is not available

Dovetail: Stronger Anonymity in Next-Generation Internet Routing

Current low-latency anonymity systems use complex overlay networks to conceal a user's IP address, introducing significant latency and network efficiency penalties compared to normal Internet usage. Rather than obfuscating network identity through higher level protocols, we propose a more direct solution: a routing protocol that allows communication without exposing network identity, providing a strong foundation for Internet privacy, while allowing identity to be defined in those higher level protocols where it adds value. Given current research initiatives advocating "clean slate" Internet designs, an opportunity exists to design an internetwork layer routing protocol that decouples identity from network location and thereby simplifies the anonymity problem. Recently, Hsiao et al. proposed such a protocol (LAP), but it does not protect the user against a local eavesdropper or an untrusted ISP, which will not be acceptable for many users. Thus, we propose Dovetail, a next-generation Internet routing protocol that provides anonymity against an active attacker located at any single point within the network, including the user's ISP. A major design challenge is to provide this protection without including an application-layer proxy in data transmission. We address this challenge in path construction by using a matchmaker node (an end host) to overlap two path segments at a dovetail node (a router). The dovetail then trims away part of the path so that data transmission bypasses the matchmaker. Additional design features include the choice of many different paths through the network and the joining of path segments without requiring a trusted third party. We develop a systematic mechanism to measure the topological anonymity of our designs, and we demonstrate the privacy and efficiency of our proposal by simulation, using a model of the complete Internet at the AS-level

대중교통 연계수단으로서 Car-hailing 도입시 최적 경로 탐색 알고리즘

학위논문(석사)--서울대학교 대학원 :공과대학 건설환경공학부,2019. 8. 고승영.Promoting the use of transit helps alleviate many problems caused by excessive use of private autos, such as traffic congestion, parking problems and air pollution. In Seoul, the modal split of transit has declined in the past five years and that of private autos has increased. This means that transit is less competitive than private autos, and in order to enhance transit competitiveness, it should first evaluate its competitiveness. Most of the studies evaluating transit focused on the accessibility of transit, which can be measured using factors such as travel time, distance and fare. This study compares the two modes by using five-weekday smart card data in Seoul to obtain the passengers of transit, and by acquiring the travel time of auto and transit through application programming (API) services. Not only travel time is compared, but the number of transit passengers is considered to define transit vulnerable ODs (Origin and Destination) in Seoul. The travel occurred during the morning peak hours where traffic is concentrated is analyzed, and the OD is selected as the transit vulnerable OD when the difference in travel time between transit and auto is more than 5 minutes and the number of passengers of transit is more than 500 in 5 days. By using four multimodal integrated route generating algorithms of each vulnerable OD, combined paths between transit and car-hailing service were generated and compared with existing unimodal paths to identify how the transit competitiveness has improved. Among the multimodal paths generated by the algorithm, the optimum path is selected by calculating the generalized cost, and the optimum paths selected by each algorithm are compared. As a result, the second algorithm, which replaces the bus with the car-hailing service and selects the transfer points before and after the transfer stations of transit path as the origin and the destination of the car-hailing service, is found to find multimodal paths most efficiently. Although the multimodal paths have the shortest travel time at a specific OD in a certain time period, at the majority of the ODs, the multimodal paths have about 30% of the travel time between the car-hailing only and the transit paths. Also, the competitiveness of multimodal path was low for ODs with short travel distance, and the competitiveness of multimodal paths was high at ODs with long travel distance. It is most effective to use the car-hailing service as transit feeder where the access time is long.대중교통의 이용을 활성화하는 것은 교통혼잡, 주차문제, 대기오염 등 과도한 승용차의 이용으로 인해 발생하는 여러 문제들을 완화하는데 도움을 준다. 서울의 경우 최근 5년동안 대중교통의 수단분담률이 감소하고 승용차의 수단분담률이 증가하고 있다. 이는 승용차 대비 대중교통의 경쟁력이 낮다는 것을 의미하고, 경쟁력을 제고하기 위해서는 먼저 대중교통의 경쟁력을 평가해야 한다. 대중교통을 평가한 대다수의 논문들은 대중교통의 접근성에 초점을 두었고, 대중교통 접근성은 통행시간, 거리, 요금 등의 요소들을 이용하여 측정할 수 있다. 본 연구는 서울시 평일 5일치 교통카드 데이터를 이용하여 대중교통의 탑승객 수를 구하고, API 서비스를 이용하여, 승용차와 대중교통의 통행시간을 구득하여, 대중교통과 승용차의 통행시간을 비교하고자 한다. 단순히 통행시간만을 비교한 것이 아니라, 해당하는 출발지와 도착지를 통행했던 대중교통 탑승객 수도 같이 고려하여 서울시의 대중교통 취약 OD를 선정한다. 통행이 집중되는 오전 첨두시에 발생한 통행을 분석하고, 대중교통과 승용차의 통행시간 차이가 5분 이상 나고, 대중교통 탑승객 수가 5일동안 500명 이상인 OD를 취약 OD로 선정한다. 선정된 취약 OD에 대하여 총 네가지의 통합 수단 경로 생성 알고리즘을 이용해 car-hailing 서비스와 대중교통이 결합된 경로를 생성하여, 기존의 단일 수단 경로와 비교하고, 대중교통 경쟁력이 얼마나 개선되는지 파악한다. 알고리즘을 이용해 생성된 통합 수단 경로들 중에서 최적 경로는 일반화 비용을 계산하여 선정하고, 알고리즘 별로 선정된 최적 경로를 비교한다. 그 결과 버스를 Car-hailing 서비스로 대체하고, 환승지점 앞, 뒤 정류장들을 Car-hailing의 출발지와 도착지로 선정하는 두번째 알고리즘이 가장 효율적으로 최적의 수단 통합 경로를 찾는 것으로 나타난다. 통합 수단 경로는 특정 시간대에 특정 OD에서는 가장 짧은 통행시간을 갖기도 하지만, 대다수의 OD에서 수단이 통합된 경로는 car-hailing만 이용한 통행과 대중교통만 이용하는 통행사이의 30% 정도 수준의 통행 시간을 갖는 것으로 나타난다. 또한 통행거리가 짧은 OD에 대해서는 통합수단의 경쟁력이 낮았고, 통행거리가 긴 OD에서 통합수단의 경쟁력이 높았다. 이를 통해 통행거리가 긴 OD 중 접근 시간이 긴 곳에 Car-hailing 서비스를 대중교통 연계수단으로 도입하는 것이 가장 효과적이라 할 수 있다.Chapter 1. Introduction 1 1.1 Background 1 1.2 Objectives 3 Chapter 2. Literature Review 4 2.1 Transit Accessibility 4 2.2 Transit Path Searching Algorithm 7 2.3 Multimodal Path Generation Algorithm 8 Chapter 3. Data and Study Area 10 3.1 Data 10 3.2 Study Area 16 Chapter 4. Methodology 18 4.1 Select Transit Vulnerable ODs 18 4.2 Multimodal Integrated Path Generation Algorithms 21 Chapter 5. Results 39 5.1 Transit Vulnerable ODs 39 5.2 Optimum Multimodal Paths 42 Chapter 6. Conclusions 62 Reference 65 국문 초록 68Maste

Three Extensions of Tong and Richardson’s Algorithm for Finding the Optimal Path in Schedule-Based Railway Networks

published_or_final_versio

Network routing and equilibrium models for urban parking search

textThis dissertation focuses on modeling parking search behavior in traffic assignment models. Parking contributes greatly to urban traffic congestion. When the parking supply is scarce, it is very common for a vehicle to circle around for a considerable period just for an open parking spot. This circling or "cruising" add additional traffic flow onto the network. However, traditional traffic assignment models either ignore parking completely or simply treat it in limited ways. Most traffic assignment models simply assume travelers just directly drive from their origin to their destination without considering the parking search behavior. This would result in a systematic underestimation of road traffic flows and congestion which may mislead traffic managers to give inappropriate planning or control strategies. Models which do incorporate parking effects either constrain their implementation in limited small networks or ignore the stochasticity of parking choice by drivers. This dissertation improves upon previous research into network parking modeling, explicitly capturing drivers' behavior and stochasticity in the parking search process, and is applicable to general networks. This dissertation constructs three types of parking search models. The first one is to model a single driver's parking search process, taking into account the likelihood of finding parking in different locations from past experience as well as observations gained during the search itself. This model uses the a priori probability of finding parking on a link, which reflects the average possibility of finding a parking space based on past experience. This probability is then adjusted based on observations during the current search. With these concepts, the parking search behavior is modeled as a Markov decision process (MDP). The primary contribution of the proposed model is its ability to reflect history dependence which combines the advantages of assuming "full reset" and "no reset" . "Full reset" assumes the probability of finding a parking space on a link is independent of any observations in the current search, while "no reset" assumes the state of parking availability is completely determined by past observations, never changing once observed. For instance, assume that the a priori probability of finding parking on a link is 30%. "Full reset" implies that if a driver drives on this link and sees no parking available, if he or she immediately turns around and drives on the link again, the probability of finding parking is again 30% independent of the past observation. By contrast, "no reset" implies that if a parking space is available on a link, it will always be available to return to in the future at any point. This dissertation develops an "asymptotic reset" principle which generalizes these principles and allows past observations to affect the probability of finding parking on a link and this impact weakens as time goes by. Both full reset and no reset are shown to be special cases of asymptotic reset. The second problem is modeling multiple drivers through a parking search equilibrium on a static network. Similar to the first type of problem, drivers aim to minimize their total travel costs. Their driving and parking search behaviors depend on the probabilities of finding parkings at particular locations in the network. On the other side, these probabilities depend on drivers' route and parking choices. This mutual dependency can be modeled as an equilibrium problem. At the equilibrium condition no driver can improve his or her expected travel cost by unilaterally changing his or her routing and parking search strategy. To accomplish this, a network transformation is introduced to distinguish between drivers searching for parking on a link and drivers merely passing through. The dependence of parking probability on flow rates results in a set of nonlinear flow conservation equations. Nevertheless, under relatively weak assumptions the existence and uniqueness of the network loading can be shown, and an intuitive 'flow-pushing" algorithm can be used to solve for the solution of this nonlinear system. Built on this network loading algorithm, travel times can be computed. The equilibrium is formulated as a variational inequality, and a heuristic algorithm is presented to solve it. An extensive set of numerical tests shows how parking availability and traffic congestion (flows and delays) vary with the input data. The third problem aims at developing a dynamic equivalent for the network parking search equilibrium problem. This problem attempts to model a similar set of features as the static model, but aims to reflect changes in input demand, congestion, and parking space availability over time. The approach described in the dissertation is complementary to the static approach, taking on the flavor of simulation more than mathematical formulation. The dynamic model augments the cell transmission model with additional state variables to reflect parking availability, and integrates this network loading with an MDP-based parking search behavior model. Finally, case studies and sensitivity analysis are taken for each of the three models. These analyses demonstrate the models' validity and feasibility for practice use. Specifically, all the models show excess travel time and flow on the transportation networks because of taking into account the "parking search cruising" and can show the individual links so affected. They all reflect the scattered parking distribution on links while traditional traffic assignment models only assign vehicles onto specified destination nodes.Civil, Architectural, and Environmental Engineerin

A multi-dimensional rescheduling model in disrupted transport network using rule-based decision making

Apart from daily recurrent traffic congestion, unforeseen events such as flood induced road damages or bridge collapses can degrade the capacity of traffic supply and cause a significant influence on travel demand. An individual realising the unexpected events would take action to reschedule its day plan in order to fit into the new circumstance. This paper analyses the potential reschedule possibilities by augmenting the Within-Day Replanning simulation model implemented in the Multi-Agent Transport Simulation (MATSim) framework. Agents can adjust day plan through multi-dimensional travel decisions including route choice, departure time choice, mode switch, trip cancellation. The enhanced model not only improves the flexibility of MATSim in rescheduling a plan during an execution day, but also lays the foundation of integrating more detailed heterogeneity decision rules into the travel behaviour simulation to cope with unexpected incidents. Furthermore, the proposed rescheduling model is capable of predicting the network performance in the real-world picture and gives a hint on how best react to transport disruptions for transport management agency

A Modular, Adaptive, and Autonomous Transit System (MAATS): A In-motion Transfer Strategy and Performance Evaluation in Urban Grid Transit Networks

Dynamic traffic demand has been a longstanding challenge for the conventional transit system design and operation. The recent development of autonomous vehicles (AVs) makes it increasingly realistic to develop the next generation of transportation systems with the potential to improve operational performance and flexibility. In this study, we propose an innovative transit system with autonomous modular buses (AMBs) that is adaptive to dynamic traffic demands and not restricted to fixed routes and timetables. A unique transfer operation, termed as “in-motion transfer”, is introduced in this paper to transfer passengers between coupled modular buses in motion. A two-stage model is developed to facilitate in-motion transfer operations in optimally designing passenger transfer plans and AMB trajectories at intersections. In the proposed AMB system, all passengers can travel in the shortest path smoothly without having to actually alight and transfer between different bus lines. Numerical experiments demonstrate that the proposed transit system results in shorter travel time and a significantly reduced average number of transfers. While enjoying the above-mentioned benefits, the modular, adaptive, and autonomous transit system (MAATS) does not impose substantially higher energy consumption in comparison to the conventional bus syste