2021

Neeraj Saxena, Australian Road Research Board
Vinayak Dixit, University of New South Wales
David Rey, University of New South Wales

Routing algorithms are often used to assign the demand on to a route connecting their origins and destinations in a network. These algorithms, which generally compute the shortest path for an OD pair, have been designed taking into consideration a single or multiple OD matrices. However, while finding the best route in a single demand period is still plausible, knowing the best path among multiple demand scenarios is quite onerous from a behavioral standpoint. This paper aims at studying driver behavior towards choosing routes across multiple demand scenarios to understand if they can identify the best route across these scenarios. An online experiment was conducted among the staff and students comprising several route choice scenarios involving multiple demand scenarios. A lottery choice experiment was also conducted to determine the risk perceptions of the participants. The results show that around one-quarter of the participants are able to identify the best routes in each of the 9 route choice treatments presented to them. It is also observed that the other one-quarter of the participants preferred the fastest route (shortest in one demand scenario) over the best route (shortest across multiple demand scenarios) across the 9 route choice treatments. The outcomes from this study provide a behavioral evidence to some of the advanced routing algorithms which are increasingly being used in transportation modeling and research.

Justice Darko, North Carolina A&T State University
Hyoshin Park, North Carolina A&T State University

Considering and integrating the unique preferences of a traveler in navigation in transit networks may improve their travel experience, especially for travelers with disabilities. This study proposes a new normative approach to improve a traveler’s door to door travel experience. In addition to known attributes of a traveler’s utility, such as waiting time, the number of transfers, and the total travel time considered in prior efforts, this study incorporates a user-preference cost function for finding the most accessible sidewalk path in all walking-only choice problem, and the travelers’ risk-tolerance for travel time uncertainties in transit links in the walk-transit-walk choice problem. Travelers’ risk-tolerance for travel time uncertainties is modeled using the exponential function approximated by the mean-variance utility function. A realistic assessment of the effect of the user-defined preferences on a travelers’ path choice is presented for a small section of the Boston transit network, with schedule data from the Massachusetts bay transit authority.

Junxiang XU, Southwest Jiaotong University
Jin Zhang, Southwest Jiaotong University
Jingni Guo, Southwest Jiaotong University
Weihua Liu, China Academy of Railway Sciences
Hui Ma, China Academy of Railway Sciences

In order to investigate the impact of travelers' adaptive adjustment behaviors on traffic network flow diversion under the assumption of bounded rationality, a multi-agent route choice model with individual interaction mechanism is established by using cumulative prospect theory and evolutionary cellular automata. In the model, travelers are divided into risk-seeking and risk-aversion ones. Based on the reliability of travel time and the idea of cellular genetic algorithm, the dynamic reference points and their evolution rules for travelers with heterogeneous characteristics are designed to enable individual travelers dynamically adjust their travel time budget according to the changes in the decision-making environment, which is more in line with the actual behavior characteristics of travelers. Finally, the evolution rule of multi-agent reference points is combined with the traditional method of successive average algorithm to design the multi-agent bounded rational route choice evolution algorithm for the solving the problem of traffic flow assignment in a road network. The research results show that the evolution model has well inherited the characteristics of the route flow diversion in the traditional model; and that the proportion of different traveler types and the information reception level of travelers are important factors that affect the diversion structure of the road network.

Amit Daly, Ben-Gurion University of the Negev
Hillel Bar-Gera, Ben Gurion University of the Negev

Vickrey’s seminal departure time choice model is based on a penalty function which is a linear combination of travel time, earliness and lateness. The original model depicts a single link from a single origin to a single destination, serving homogeneous travelers by a deterministic point-queue regime. Numerous variants of the basic model, relaxing one or more of these assumption, have been used in a wide range of contexts. The equilibrium solution of the basic model can be computed directly by exact formula. Specific convergent methods have been proposed for certain variants.   One of the troubling challenges regarding this model is the need for a generic iterative numeric approach, that may address complex models in which departure time choice is embedded. Natural candidates were shown to fail even on the basic model.   In this paper we explore a fairly naive approach, where in each iteration demand is shifted from the maximum cost time interval to the minimum cost time interval. Results for the basic model are promising, demonstrating that with a fixed shift solutions converge to a deviation which is proportional to the shift size, and that semi-adaptive or adaptive shift size may offer convergence to any desirable level of approximation of the exact equilibrium.

Kayvan Bagheri, University of Tehran
Najmeh Neysani Samany, University of Tehran
Ara Toomanian, University of Tehran
Mohammadreza Jelokhani Niaraki, University of Tehran
Leila Hajibabai (lhajiba@ncsu.edu), North Carolina State University

Medical solid wastes are major hazardous matters that impose risks to a society’s public and environmental health due to exposure to harmful biological or chemical compounds. Environmentally-sound medical waste management can prevent potential health risks to the general public, particularly in large urban settings. Waste collection is a significant functional component in recent waste management systems and may account for up to half of the total outlay on waste management in big cities. This paper proposes a routing scheme for optimal collection of medical wastes powered by geo-spatial information system (GIS) capabilities. The problem is first formulated as a mixed-integer linear program in the road network for spatial data that aims to (i) minimize total routing cost of medical solid waste collection and transfer to waste landfills and, (ii) balance the workload across waste collectors. In this study, a planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. Then, an iterative cluster-first-route-second heuristic is utilized to solve the proposed routing problem which customizes a K -means algorithm to determine the optimal number and size of clusters (i.e., routes). A traveling salesman problem algorithm is then applied to each cluster to regulate the optimal sequence of visits to medical centers. The numerical experiments have been implemented in a case study in Tehran (a megacity with 505 nodes and 142,000 links). The results were compared to benchmark cases that show improvements in balancing collectors’ workload (i.e., ~4 min reduction in standard deviation of average travel time) and reductions in travel time (i.e., an average ~1 hr for the entire fleet and ~4 min per route). The experiments confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes to reduce costs and environmental impacts.

Venktesh Pandey (vpandey@ncat.edu), North Carolina A&T State University

Express lane facilities provide toll and travel time information in real time through variable message signs or phone apps. In contrast to the assumptions in the adaptive routing literature, the online travel time or toll information is commonly incomplete, error-prone, or aggregated for the entire route. In this article, we propose algorithms that find the least-expected-cost routing strategy from an origin to a destination given partial information at different diverge locations. Formulating the problem as a partially observable Markov decision process (POMDP), we study two iterative algorithms, namely batch enumeration and incremental pruning, that compute the optimal value functions for the POMDP and thus determine the optimal routing strategy. Analysis on different test networks shows that incremental pruning outperforms batch enumeration by efficiently reducing the search space of possible strategies at any node. Additionally, relative to the case of routing under complete online information, providing no information increases the expected travel cost by up to 16% whereas providing only toll information increases the expected travel cost by up to 2.5% suggesting the effectiveness of only displaying toll information on express lanes. The proposed routing algorithms are useful for adaptive route-recommender software on express lanes and for use in the planning and pricing models for these lanes.

Murat Bayrak, Pennsylvania State University
Vikash Gayah (gayah@engr.psu.edu), Pennsylvania State University

Restricting left turns throughout a network improves overall flow capacity by reducing conflicts between left-turning and through-moving vehicles. However, doing so comes with the drawback of requiring vehicles to travel longer distances on average. Implementing these restrictions at only a subset of locations can help by balancing this tradeoff between increased capacity and longer trips. Unfortunately, identifying exactly where these restrictions should be implemented is a complex problem due to the very large number of configurations that must be tested and interdependencies between left-turn restriction decisions at adjacent intersections. This paper implements three heuristic solution algorithms—population-based incremental learning, Bayesian optimization and a hybrid of the two—to identify optimal locations of left-turn restrictions at individual intersections in a grid network. Scenarios are tested in which restriction decisions are the same for all intersection approaches and in which this decision is only the same for approaches in the same direction. The latter case is particularly complex as it increases the number of potential configurations exponentially. The results suggest all methods can be effectively used to solve this problem, though the population-based incremental learning method appears to perform the best in the more complex scenario. The proposed framework and procedures can be applied to realistic city networks to identify where left-turn restrictions should be implemented to improve overall network operations.

Christopher Cummings, Northwestern University
Hoseb Abkarian, Northwestern University
Yuhan Zhou, Northwestern University
Divyakant Tahlyan, Northwestern University
Karen Smilowitz, Northwestern University
Hani Mahmassani, Northwestern University

Large-scale planned special events (PSEs) can pose unique transportation and logistics challenges. Data collection and simulation are important tools to address these challenges, although they are often difficult due to event size and complexity. This paper discusses methods to address the twin challenges of data collection and simulation at large PSEs through the context of AirVenture, a large week-long airshow organized by Experimental Aircrafts Association (EAA) in Oshkosh, Wisconsin. Sampling and data collection techniques are discussed for a variety of modal conveyances like private vehicles, pedestrians and shuttles, and for different situations like vehicle arrivals and departures, pedestrian queues, and shuttle systems. A simulation framework for integrating these three modes and numerous activities is developed as a network of heterogeneous queues and queue-dependent choices. The results of this study demonstrate the effectiveness and flexibility of the data collection and simulation methodologies. The techniques developed in this work can be used to improve planning and transportation systems at many other forms of PSEs.

Haris Ballis, University of Cyprus
Loukas Dimitriou, University of Cyprus

Origin-Destination matrices (OD) prove as a particularly suitable mean for the aggregate representation of movements or flows between pairs of locations. Nonetheless, their aggregate nature, cannot represent interdependencies among the contained movements, an element of immense value for analysing mobility in networks. However, it is possible to infer such information by the conversion of ODs to graphs, allowing the expression of the interdependencies between trips (i.e. trip-chains) as simple graph paths. Therefore, the problem of converting an OD to trip-chains then transforms to the enumeration of all paths within the graph and the identification of their combination which recreates the given OD. However, the required enumeration process can prove impractical for large-scale graphs. The currently presented study suggests a methodology, able to optimally confine the resulting number of identified paths without discarding those required for the recreation of the inputted OD. The effectiveness of the proposed simplification process is proven on a realistic large-scale graph deriving from the aggregation of thousands of observed tours. Promising results showcase the ability of the methodology to accurately identify the observed tours (85% accuracy) within the input graph without excessive requirements in processing time or memory size.

Muhammad Habib, Dalhousie University
MD Jahedul Alam, Dalhousie University
Devin Holmes, Dalhousie University

This research attempts to understand the impacts of social distancing regulations on pedestrian environments from the perspective of traffic flow and business activity. This study uses a market demand pedestrian simulation model to understand how pedestrians move on the busy street of Spring garden Road, under various scenarios. The three scenarios tested in this study observe urban environments that eastern Canada has experience during the pandemic. The business-as-usual scenario will simulate traffic flow under no social distancing regulations. Pandemic scenario 1 & 2 will use adjusted parameters to encourage social distancing. The sidewalk width in the second pandemic scenario is extended to represent the actions of the municipality as per the mobility response plan for the city of Halifax. The results show that social distancing regulations in the pandemic scenarios significantly improved traffic flow in terms of the reduction in pedestrian contact violations. These violations are described as instances in which pedestrian violate the 2m social distancing rule. The simulation of the first pandemic scenario (no sidewalk enhancement) showed a significant reduction of 43% in the number of contact violations during the one-hour pedestrian simulation of the street. The second pandemic scenario showed a 68% decrease in violations. The conclusions derived from this research support the actions of the municipality as the simulation results indicate that an increase in sidewalk width can influence contact rates and time travelled. When comparing the two pandemic scenarios, the scenario that incorporated wider sidewalks showed a decrease in total travel time and contact rates.

Can Gokalp, University of Texas, Austin
Priyadarshan Patil, University of Texas, Austin
Stephen Boyles (sboyles@austin.utexas.edu), University of Texas, Austin

Natural disasters cause significant disruption in road networks, rendering many crucial links unusable.  To prioritize repairs of these links, we investigate how to identify a link repair sequence which minimizes total travel time over the repair horizon, given that at each repair stage road traffic distributes according to the principle of user equilibrium.  We derive an analogue of Bellman's optimality principle, allowing us to solve the problem using methods of dynamic programming.  We specifically develop a bidirectional search heuristic with customized pruning and branching strategies that exploit specific properties of traffic assignment.  Our experiments show that our method is scalable and performs well even on networks involving thousands of links.

Xinwu Qian (xinwu.qian@ua.edu), University of Alabama, Tuscaloosa
Jiawei Xue, Purdue University
Satish Ukkusuri, Purdue University

The study proposes a modeling framework for investigating the disease dynamics with adaptive human behavior during a disease outbreak, considering the impacts of both local observations and global information. One important application scenario is that commuters may adjust their behavior upon observing the symptoms and countermeasures from their physical contacts during travel, thus altering the trajectories of a disease outbreak. We introduce the heterogeneous mean-field (HMF) approach in a multiplex network setting to jointly model the spreading dynamics of the infectious disease in the contact network and the dissemination dynamics of information in the observation network. The disease spreading is captured using the classic susceptible-infectious-susceptible (SIS) process, while an SIS-alike process models the spread of awareness termed as unaware-aware-unaware (UAU). And the use of multiplex network helps capture the interplay between disease spreading and information dissemination, and how the dynamics of one may affect the other. Theoretical analyses suggest that there are three potential equilibrium states, depending on the percolation strength of diseases and information. The dissemination of information may help shape herd immunity among the population, thus suppressing and eradicating the disease outbreak. Finally, numerical experiments using the contact networks among metro travelers are provided to shed light on the disease and information dynamics in the real-world scenarios and gain insights on the resilience of transportation system against the risk of infectious diseases.

Monika Filipovska, Northwestern University
Hani Mahmassani, Northwestern University
Archak Mittal, Ford Motor Company

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective the performance of the network is experienced at the level of a path, and as such the knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the Monte Carlo simulation approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches and its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a Monte Carlo simulation approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

Amir Mirheli, North Carolina State University
Leila Hajibabai (lhajiba@ncsu.edu), North Carolina State University

This paper investigates the optimal electric vehicle (EV) charging network design and management strategy with user-specific decisions in an equilibrium condition. A hierarchical formulation is developed with the EV charging network design and demand-driven pricing scheme in the upper level and  users' charging decisions with respect to charging expenses and travel costs in the lower level. The model aims to minimize facility deployment and operating costs as well as user-specific costs. A stochastic queuing theory is utilized to establish a linear relationship between each facility's occupancy and physical capacity and estimate the waiting time at facilities to get served. The binary location variables are redefined as continuous variables. Then, Karush-Kuhn-Tucker conditions are derived to convert the proposed bi-level program into an equivalent single-level formulation considering the lower-level objective function as complementary equations. An iterative active-set based solution technique is implemented to determine the strategic decisions on charging network design.  Additionally, the link travel times are estimated using a macroscopic fundamental diagram concept. The proposed integrated methodology is applied to a hypothetical and an empirical case study to evaluate its performance and solution quality. The numerical results indicate that the proposed algorithm can solve the problem efficiently and outperform a global bi-level optimization benchmark. Finally, a series of sensitivity analyses has been conducted to study the impact of input parameters on the solutions and draw managerial insights.

Zhoutong Jiang, University of Illinois, Urbana-Champaign
Yanfeng Ouyang (yfouyang@illinois.edu), University of Illinois, Urbana-Champaign

Strategic positioning and allocation of emergency responders and/or resources to potential emergency incidents are very important decisions for disaster management programs. In this paper, a reliable multi-type joint-service facility location model is proposed, which takes into consideration the need for cooperative service from multiple types of responder stations, as well as the probabilistic risk of station disruptions. The problem is formulated as a mixed-integer non-linear program and solved via a set of customized linear program and Lagrangian relaxation based algorithms. Numerical experiments on hypothetical and full-scale cases are conducted to demonstrate the applicability of the model and to draw managerial insights.

Bingqing Liu, New York University
Joseph Chow, New York University

With rapid development of Mobility-as-a-Service systems, unique properties of these systems that are different from traditional urban transportation systems is getting more and more attention. One of these properties is congestible capacity, which refers to the phenomenon that capacities of links are influenced by multiple inbound and outbound flows from other links. This study proposed a logit-based SUE formulation for systems with congestible capacities using a system efficiency matrix to capture the supply-side relationships to flow. The proof for equivalence of the formulation and SUE is given, which shows how the relative path delays can be obtained through the Lagrange multipliers of the capacity constraints. Due to non-separability, Lagrange multipliers may not equate to link delays, but conditions exist to derive unique Lagrange multipliers and unique SUE flows. A solution algorithm that iteratively increases the restricted path set with certain tolerance in the change of the objective value is proposed, which can eliminate unreasonable paths to different extents by changing the tolerance. Two numerical examples are provided to illustrate how the model captures the effect of congestible capacities and sensitivity to changes in the system efficiency matrix. Such a model can be used to provide a steady state of highly dynamic MaaS systems under certain demand conditions, which could facilitate the decision making of MaaS operators on schedule design and fleet deployment.

Zhaoyao Bao, Shanghai Jiao Tong University
Chi Xie (dr.chi.xie@outlook.com), Tongji University

The occurrence of disruptions in transportation networks may result in severe systematic performance degradations. This paper proposes and applies a set of new network disruption evaluation methods considering different disruption information availabilities subject to different information collection and transmission capabilities. Two typical information availability situations, namely, information globally available (IGA) and information locally available (ILA) are defined and analyzed for disrupted networks in different perception capabilities or network environments, respectively. The IGA situation implies that travelers know all the relevant disruption information to the network before their departures, while the ILA situation represents that travelers can only observe or notice the occurrence of a disruption when they reach in person the upstream node of the disrupted link. Specifically, when travelers can perceive travel costs accurately, the total travel cost over the network in the ILA situation is equal to or greater than that in the IGA situation, and the total travel cost over the network in the IGA situation is equal to or greater than the total cost without any disruption. However, when travelers perceive travel costs with random errors, similar conclusions cannot be drawn in terms of the actual cost but the expected perceived cost. The proposed set of evaluation methods are tested and evaluated on the Sioux Falls network and the Anaheim network, in which the computational results prove that the Monte Carlo simulation-based method is more efficient in the evaluation of large-scale networks with multiple probabilistically disrupted links.

Yuntian Deng (deng.556@osu.edu), Ohio State University
Shiping Shao, Ohio State University
Archak Mittal, Ford Motor Company
Richard Twumasi-Boakye, Ford Motor Company
James Fishelson, Ford Motor Company
Abhishek Gupta, Ohio State University
Ness Shroff, Ohio State University

Consider the situation where passengers travel on a multi-modal transportation network with different service providers. Passengers have to pay distinct providers at various places if several modes are involved in one trip. If some providers cooperate to offer a new multi-modal service, then it will attract more users because the price can be reduced and convenience is improved by allowing one payment for the entire trip. Based on this idea, we integrate cooperative game theoretic approaches in the hyperpath-based stochastic user equilibrium framework of a multi-modal network. A two time-scale stochastic approximation algorithm is developed to encourage the competing providers to cooperate and maximize the platform's profit through incentives. We design a fair policy for profit sharing among competing modes or providers, which ensures each provider has a profit increase after cooperation.

Shiyu Shen, University of Illinois, Urbana-Champaign
Yanfeng Ouyang, University of Illinois, Urbana-Champaign
Shuai Ren, DiDi Chuxing
Luyun Zhao, DiDi Chuxing

Demand Responsive Transit (DRT) has the potential to provide passengers with higher accessibility and lower travel time as compared to conventional transit, and at the same time make more efficient use of vehicle capacity than traditional taxi. In many current systems, vehicles are assigned to passengers along travel paths that are chosen myopically. When information on future demand distribution is available, it would be more beneficial to strategically dispatch transit vehicles to areas with a higher probability of generating passengers. This paper proposes a mathematical model for a dynamic DRT vehicle dispatch problem. It determines in real time how the operating vehicles shall be used to serve arriving passenger demand, and which paths should the vehicles choose to achieve a balance between operator and passenger costs. The model is solved by an approximate dynamic programming (ADP) based solution approach. Case studies, including a hypothetical numerical example and a real-world case in Qingdao, China, have been conducted to demonstrate the applicability of the proposed modeling framework. Results show that the proposed ADP solution can significantly improve the overall system performance as compared to myopic benchmarks.