An Introduction to MultiAgent Systems.

Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever- evolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.

Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective

The rapid increase in the cyber-physical nature of transportation, availability of GPS data, mobile applications, and effective communication technologies have led to the emergence of On-Demand Transit (ODT) systems. In September 2018, the City of Belleville in Canada started an on-demand public transit pilot project, where the late-night fixed-route (RT 11) was substituted with the ODT providing a real-time ride-hailing service. We present an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin and destination patterns of Belleville ODT users, based on the data collected from September 2018 till May 2019. The independent and combined effects of the demographic characteristics (population density, working-age, and median income) on the ODT trip production and attraction levels were studied using GIS and the K-means machine learning clustering algorithm. The results indicate that ODT trips demand is highest for 11:00 pm-11:45 pm during the weekdays and 8:00 pm-8:30 pm during the weekends. We expect this to be the result of users returning home from work or shopping. Results showed that 39% of the trips were found to have a waiting time of smaller than 15 minutes, while 28% of trips had a waiting time of 15-30 minutes. The dissemination areas with higher population density, lower median income, or higher working-age percentages tend to have higher ODT trip attraction levels, except for the dissemination areas that have highly attractive places like commercial areas.

Spatio-Temporal Analysis of On Demand Transit: A Case Study of Belleville, Canada

This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently.

Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore

Emerging transportation network services, such as customized buses, hold the promise of expanding overall traveler accessibility in congested metropolitan areas. A number of internet-based customized bus services have been planned and deployed for major origin-destination (OD) pairs to/from inner cities with limited physical road infrastructure. In this research, we aim to develop a joint optimization model for addressing a number of practical challenges for providing flexible public transportation services. First, how to maintain minimum loading rate requirements and increase the number of customers per bus for the bus operators to reach long-term profitability. Second, how to optimize detailed bus routing and timetabling plans to satisfy a wide range of specific user constraints, such as passengers’ pickup and delivery locations with preferred time windows, through flexible decision for matching passengers to bus routes. From a space-time network modeling perspective, this paper develops a multi-commodity network flow-based optimization model to formulate a customized bus service network design problem so as to optimize the utilization of the vehicle capacity while satisfying individual demand requests defined through space-time windows. We further develop a solution algorithm based on the Lagrangian decomposition for the primal problem and a space-time prism based method to reduce the solution search space. Case studies using both the illustrative and real-world large-scale transportation networks are conducted to demonstrate the effectiveness of the proposed algorithm and its sensitivity under different practical operating conditions.

Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

Due to advances in communications technologies and social networks, flexible mobility systems such as taxi, carpool and demand responsive transit have gained interest among practitioners and researchers as a solution to address such problems as the ``first/last mile problem". While recent research has modeled these systems using agent-based stochastic day-to-day processes, they assume only traveler adjustment under a one-sided market setting. What if such systems are naturally ``two-sided markets" like Uber or AirBnB? In this study, we explore flexible transport services in the framework of two-sided markets, and extend an earlier day-to-day adjustment process to include day-to-day adjustment of the service operator(s) as the seller and the built environment as the platform of a two-sided market. We use the Ramsey pricing criterion for social optimum to show that a perfectly matched state from a day-to-day process is equivalent to a social optimum. A case study using real data from Oakville, Ontario, as a first/last mile problem example demonstrates the sensitivity of the day-to-day model to operating policies. Computational experiments confirm the existence of locally stable states. More importantly, the experiments show the existence of thresholds from which network externalities cause two-sided and one-sided market equilibria to diverge.

An agent-based day-to-day adjustment process for modeling ‘Mobility as a Service’ with a two-sided flexible transport market

The rapid increase in the cyber-physical nature of transportation, availability of GPS data, mobile applications, and effective communication technologies have led to the emergence of On-Demand Transit (ODT) systems. In September 2018, the City of Belleville in Canada started an on-demand public transit pilot project, where the late-night fixed-route (RT 11) was substituted with the ODT providing a real-time ride-hailing service. We present an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin and destination patterns of Belleville ODT users, based on the data collected from September 2018 till May 2019. The independent and combined effects of the demographic characteristics (population density, working-age, and median income) on the ODT trip production and attraction levels were studied using GIS and the K-means machine learning clustering algorithm. The results indicate that ODT trips demand is highest for 11:00 pm–11:45 pm during the weekdays and 8:00 pm–8:30 pm during the weekends. We expect this to be the result of users returning home from work or shopping. Results showed that 39% of the trips were found to have a waiting time of smaller than 15 min, while 28% of trips had a waiting time of 15–30 min. The dissemination areas with higher population density, lower median income, or higher working-age percentages tend to have higher ODT trip attraction levels, except for the dissemination areas that have highly attractive places like commercial areas. For the sustainable deployment of ODT services, we recommend (a) proactively relocating the empty ODT vehicles near the neighbourhoods with high level of activity, (b) dynamically updating the fleet size and location based on the anticipated changes in the spatio-temporal demand, and (c) using medium occupancy vehicles, like vans or minibuses to ensure high level of service.

Spatio-temporal analysis of on-demand transit: A case study of Belleville, Canada

Advances in information and communications technologies, connected vehicle technologies, and Big Data have made it viable for public agencies to offer efficient flexible transit services for travel demand that is predominantly dynamic to the system. There is a clear gap in methodologies to evaluate the user equilibrium for flexible transport services (FTS). In this study we lay the groundwork for studying the equilibrium of these systems and propose an agent-based adjustment process to evaluate the properties of a stable state as an agent-based stochastic user equilibrium. To validate the proposed process and illustrate its effectiveness in measuring the effect of changes in FTS operating parameters on ridership three sets of experiments are conducted: (1) illustration with a simple 2-link network, (2) evaluation of a dynamic dial-a-ride problem, and (3) illustration using real data from Oakville, Ontario consisting of 57 zones and 2000 commuters.

Agent-based day-to-day adjustment process to evaluate dynamic flexible transport service policies

The demand for goods and services in North America has increased dramatically over the past few decades. This demand increase resulted in an associated increase in truck traffic on North American highways, escalating congestion, operation, and safety concerns. This in turn has led to increasing interest in strategies to reduce interaction between trucks and cars, including truck restrictions and dedicated truck lanes. The purpose of this paper is to implement algorithms for evaluating safety measures for different scenarios of truck lane restrictions and dedicated truck lanes using microscopic traffic simulation. The measures of performance calculated in this study are lane changing, merging, and rear-end conflicts. These measures are analyzed for truck-restricted lanes and dedicated truck lanes on the Gardiner Expressway in downtown Toronto, Canada. Simulation scenarios are developed by varying lane strategies and truck percentage. Simulation results showed that implementation of a single truck-restricted lane makes little difference to most conflict measures. Restricting trucks from the leftmost two lanes results in more substantial reductions in lane changing conflicts, but causes some increased freeway merging conflicts involving trucks. Dedicating the leftmost lane only to trucks also reduces lane changing conflicts but increases lane merging conflicts. Because lane changing conflicts are far more frequent than merging conflicts, there appears to be a net safety benefit by either restricting trucks from the left two lanes or dedicating the left lane to trucks. Relationships between lane strategy and rear-end conflicts are weak. Truck lane strategies are most effective when truck percentage exceeds 15%.

Shadi Djavadian

Papers

Spatio-Temporal Analysis of On Demand Transit: A Case Study of Belleville, Canada

An agent-based day-to-day adjustment process for modeling ‘Mobility as a Service’ with a two-sided flexible transport market

Spatio-temporal analysis of on-demand transit: A case study of Belleville, Canada

Agent-based day-to-day adjustment process to evaluate dynamic flexible transport service policies

Safety Evaluation of Truck Lane Restriction Strategies Using Microsimulation Modeling