On the Co-Design of AV-Enabled Mobility Systems

The design of autonomous vehicles (AVs) and the design of AV-enabled mobility systems are closely coupled. Indeed, knowledge about the intended service of AVs would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management decisions. This calls for tools to study such a coupling and co-design AVs and AV-enabled mobility systems in terms of different objectives. In this paper, we instantiate a framework to address such co-design problems. In particular, we leverage the recently developed theory of co-design to frame and solve the problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby AVs service travel demands jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. To showcase our methodology, we present a real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess costs and benefits of interventions, and that such analytical techniques might gain a momentous role in policy-making in the future.Comment: 8 pages, 4 figures. Published in the Proceeding of the 23rd IEEE Intelligent Transportation Systems Conference, ITSC 2020. arXiv admin note: substantial text overlap with arXiv:1910.07714, arXiv:2008.0897

Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterize the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems

Sustainability of Autonomous Vehicles: An Agent-based Simulation of the Private Passenger Sector

Initiatives such as the European Green Deal establish mandatory objectives for climate neutrality by 2050. To realize this, predominant sectors like the transportation industry necessitate substantial improvements in emission efficiency, as transportation accounts for 20% of global CO2 emissions. Remarkably, 41 % of that are attributed to passenger transport alone. Amidst technological progression, shared autonomous vehicles (SAV) are projected to be a vital instrument in reducing greenhouse gas emissions in the private passenger sector. Hence, this study investigates the potential sustainability advantages of SAV introduction to the private passenger car sector. We draw upon an agent-based simulation model to avoid building upon theoretic populations and generic simulation approaches, not appropriately allowing to derive realistic SAV simulations. Agent-based simulations account for individual agent optimisation and are posed to be especially applicable to model traffic. More precisely, we build upon a calibrated model which incorporates real world commuter and travel statistics of Berlin. As current research frequently uses outdated travel data, this study generates a projection of three levels of travel demand of the wider area of Berlin in 2050. Additionally, we identify the need for more research on sustainability effects considering multiple levels of potential SAV introduction, as AV and SAV adoption is associated with high uncertainty. Regulatory interventions posed to be a solution to steer SAV adoption effectively. Therefore, we introduce three levels of SAV-exclusive car-based traffic zones in our simulation scenarios. The three level of zones range from the inner city of Berlin to the entire simulation, including Berlin and Brandenburg. Lastly, we identify the need for more comprehensive sustainability analyses, focusing on more than single parameters such as tailpipe emissions. Consequently, we compare driving-related emissions and energy consumption as well as the total expected life-cycle greenhouse gas impact of all SAV introduction and demand forecasting scenarios. Our findings reveal that SAV introduction increases the total passenger travel duration up to 62.1% and the passenger travel distance of up to 15.2% due to added wait time and detours. This effect is particularly noticeable in large SAV-exclusive zones. However, occupancy rates increase simultaneously, causing total vehicles kilometers to stay consistent. We observe an initial rise of 0.8% to 2.8% in vehicle kilometers considering an unchanged population, while smaller SAV-exclusive zones see the highest increase. In turn, when including our travel demand forecast which accounts for increased population size and travel density, SAV introduction reduces total vehicle kilometers by 0.5% to 3.6%, related to higher SAV occupancy. Lastly, accounting for SAV-related efficiency increases, we conclude savings in total life-cycle CO2 emissions ranging from 0.4% to 9.6% and energy consumption ranging from 1.5% to 12.2% across all scenarios. When combined with a fully electric SAV fleet, the potential for emission reduction increases to 59.0%, and for energy consumption reduction to 74.7%

Simulation study on the fleet performance of shared autonomous bicycles

Rethinking cities is now more imperative than ever, as society faces global challenges such as population growth and climate change. The design of cities can not be abstracted from the design of its mobility system, and, therefore, efficient solutions must be found to transport people and goods throughout the city in an ecological way. An autonomous bicycle-sharing system would combine the most relevant benefits of vehicle sharing, electrification, autonomy, and micro-mobility, increasing the efficiency and convenience of bicycle-sharing systems and incentivizing more people to bike and enjoy their cities in an environmentally friendly way. Due to the uniqueness and radical novelty of introducing autonomous driving technology into bicycle-sharing systems and the inherent complexity of these systems, there is a need to quantify the potential impact of autonomy on fleet performance and user experience. This paper presents an ad-hoc agent-based simulator that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in realistic scenarios, including a rebalancing system based on demand prediction. In addition, this work describes the impact of different parameters on system efficiency and service quality and quantifies the extent to which an autonomous system would outperform current bicycle-sharing schemes. The obtained results show that with a fleet size three and a half times smaller than a station-based system and eight times smaller than a dockless system, an autonomous system can provide overall improved performance and user experience even with no rebalancing. These findings indicate that the remarkable efficiency of an autonomous bicycle-sharing system could compensate for the additional cost of autonomous bicycles

Real-Time Optimization for Dynamic Ride-Sharing

Throughout the last decade, the advent of novel mobility services such as ride-hailing, car-sharing, and ride-sharing has shaped urban mobility. While these types of services offer flexible on-demand transportation for customers, they may also increase the load on the, already strained, road infrastructure and exacerbate traffic congestion problems. One potential way to remedy this problem is the increased usage of dynamic ride-sharing services. In this type of service, multiple customer trips are combined into share a vehicle simultaneously. This leads to more efficient vehicle utilization, reduced prices for customers, and less traffic congestion at the cost of slight delays compared to direct transportation in ride-hailing services. In this thesis, we consider the planning and operation of such dynamic ride-sharing services. We present a wider look at the planning context of dynamic ride-sharing and discuss planning problems on the strategical, tactical, and operational level. Subsequently, our focus is on two operational planning problems: dynamic vehicle routing, and idle vehicle repositioning. Regarding vehicle routing, we introduce the vehicle routing problem for dynamic ridesharing and present a solution procedure. Our algorithmic approach consists of two phases: a fast insertion heuristic, and a local search improvement phase. The former handles incoming trip requests and quickly assigns them to suitable vehicles while the latter is responsible for continuously improving the current routing plan. This way, we enable fast response times for customers while simultaneously effectively utilizing available computational resources. Concerning the idle vehicle repositioning problem, we propose a mathematical model that takes repositioning decisions and adequately reflects available vehicle resources as well as a forecast of the upcoming trip request demand. This model is embedded into a real-time planning algorithm that regularly re-optimizes the movement of idle vehicles. Through an adaptive parameter calculation process, our algorithm dynamically adapts to changes in the current system state. To evaluate our algorithms, we present a modular simulation-based evaluation framework. We envision that this framework may also be used by other researchers and developers. In this thesis, we perform computational evaluations on a variety of scenarios based on real-world data from Chengdu, New York City, and Hamburg. The computational results show that we are able to produce high-quality solutions in real-time, enabling the usage in high-demand settings. In addition, our algorithms perform robustly in a variety of settings and are quickly adapted to new application settings, such as the deployment in a new city