Substituting individual mobility by mobility on demand using autonomous vehicles - a sustainable assessment simulation of Berlin and Stuttgart

2020 ◽  
Vol 20 (4) ◽  
pp. 369
Author(s):  
Eliane Horschutz Nemoto ◽  
Inna Morozova ◽  
Ralf Wörner ◽  
Ines Jaroudi ◽  
Guy Fournier ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
On Demand ◽  
Individual Mobility

scholarly journals Cybersafety Approach to Cybersecurity Analysis and Mitigation for Mobility-as-a-Service and Internet of Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1220
Author(s):  
Chee Wei Lee ◽  
Stuart Madnick
Keyword(s):  
Autonomous Vehicles ◽  
Process Analysis ◽  
Hazard Analysis ◽  
Urban Mobility ◽  
Internet Of Vehicles ◽  
On Demand ◽  
Software Updates ◽  
Levels Of Automation ◽  
New Generation ◽  
Convergence Of Technologies

Urban mobility is in the midst of a revolution, driven by the convergence of technologies such as artificial intelligence, on-demand ride services, and Internet-connected and self-driving vehicles. Technological advancements often lead to new hazards. Coupled with the increased levels of automation and connectivity in the new generation of autonomous vehicles, cybersecurity is emerging as a key threat affecting these vehicles. Traditional hazard analysis methods treat safety and security in isolation and are limited in their ability to account for interactions among organizational, sociotechnical, human, and technical components. In response to these challenges, the cybersafety method, based on System Theoretic Process Analysis (STPA and STPA-Sec), was developed to meet the growing need to holistically analyze complex sociotechnical systems. We applied cybersafety to coanalyze safety and security hazards, as well as identify mitigation requirements. The results were compared with another promising method known as Combined Harm Analysis of Safety and Security for Information Systems (CHASSIS). Both methods were applied to the Mobility-as-a-Service (MaaS) and Internet of Vehicles (IoV) use cases, focusing on over-the-air software updates feature. Overall, cybersafety identified additional hazards and more effective requirements compared to CHASSIS. In particular, cybersafety demonstrated the ability to identify hazards due to unsafe/unsecure interactions among sociotechnical components. This research also suggested using CHASSIS methods for information lifecycle analysis to complement and generate additional considerations for cybersafety. Finally, results from both methods were backtested against a past cyber hack on a vehicular system, and we found that recommendations from cybersafety were likely to mitigate the risks of the incident.

scholarly journals Local Climate Action Planning as a Tool to Harness Greenhouse Gas Emissions Mitigation and the Equity Potential of Autonomous Vehicles and On-Demand Mobility

2021 ◽  
pp. 036119812110525
Author(s):  
Serena Alexander ◽  
Asha Weinstein Agrawal ◽  
Benjamin Clark
Keyword(s):  
Content Analysis ◽  
Greenhouse Gas ◽  
Public Transportation ◽  
Autonomous Vehicles ◽  
Ghg Emissions ◽  
Action Planning ◽  
Transportation System ◽  
Emissions Reduction ◽  
On Demand ◽  
Climate Action

This paper focuses on how cities can use climate action plans (CAPs) to ensure that on-demand mobility and autonomous vehicles (AVs) help reduce, rather than increase, greenhouse gas (GHG) emissions and inequitable impacts from the transportation system. We employed a three-pronged research strategy involving: (1) an analysis of the current literature on on-demand mobility and AVs; (2) a systematic content analysis of 23 CAPs and general plans (GPs) developed by municipalities in California; and (3) a comparison of findings from the literature and content analysis of plans to identify opportunities for GHG emissions reduction and mobility equity. Findings indicate that policy and planning discussions should consider the synergies between AVs and on-demand mobility as two closely related emerging mobility trends, as well as the key factors (e.g., vehicle electrification, fuel efficiency, use and ownership, access, and distribution, etc.) that determine whether the deployment of AVs would help reduce GHG emissions from transportation. Additionally, AVs and on-demand mobility have the potential to contribute to a more equitable transportation system by improving independence and quality of life for individuals with disabilities and the elderly, enhancing access to transit, and helping alleviate the geographic gap in public transportation services. Although many municipal CAPs and GPs in California have adopted several strategies and programs relevant to AVs and on-demand mobility, several untapped opportunities exist to harness the GHG emissions reduction and social benefits potential of AVs and on-demand mobility.

scholarly journals Rebalancing Autonomous Vehicles using Deep Reinforcement Learning

2022 ◽  
Vol 16 ◽  
pp. 646-652
Author(s):  
Jiajie Dai ◽  
Qianyu Zhu ◽  
Nan Jiang ◽  
Wuyang Wang
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Supply And Demand ◽  
Efficient Operation ◽  
Demand And Supply ◽  
On Demand ◽  
Demand Information ◽  
Autonomous Mobility ◽  
Demand Patterns

The shared autonomous mobility-on-demand (AMoD) system is a promising business model in the coming future which provides a more efficient and affordable urban travel mode. However, to maintain the efficient operation of AMoD and address the demand and supply mismatching, a good rebalancing strategy is required. This paper proposes a reinforcement learning-based rebalancing strategy to minimize passengers’ waiting in a shared AMoD system. The state is defined as the nearby supply and demand information of a vehicle. The action is defined as moving to a nearby area with eight different directions or staying idle. A 4.6 4.4 km2 region in Cambridge, Massachusetts, is used as the case study. We trained and tested the rebalancing strategy in two different demand patterns: random and first-mile. Results show the proposed method can reduce passenger’s waiting time by 7% for random demand patterns and 10% for first-mile demand patterns.

China-US driverless tech will transform urban transit

2016 ◽  
Keyword(s):  
Public Transport ◽  
Autonomous Vehicles ◽  
Business Models ◽  
Road Traffic ◽  
The United States ◽  
Harm Minimisation ◽  
Urban Transit ◽  
Content Type ◽  
On Demand ◽  
Transport Services

Significance China and the United States, the world's two largest car markets, are both pursuing leadership in developing autonomous vehicles and the much-touted transformation such transport will bring -- 'social mobility services'. Impacts Governments could save on road traffic infrastructure as a result of self-driving vehicles optimising existing road capacity. On-demand shared transport services will increasingly replace scheduled public transport. Multi-modal medium-distance on-demand public transport will develop, challenging the business models of airlines and railways. Harm-minimisation programming poses ethical and liability issues for driverless vehicle manufacturers, operators and owners.

A reputation-based framework to support dynamic car-pooling1,2

2020 ◽  
Vol 14 (1) ◽  
pp. 89-102
Author(s):  
Maria Nadia Postorino ◽  
Giuseppe M.L. Sarnè
Keyword(s):  
Time Slot ◽  
Routing Algorithm ◽  
Simulated Data ◽  
Urban Traffic ◽  
Traffic Flows ◽  
On Demand ◽  
Road Congestion ◽  
Individual Mobility ◽  
Multi Agent ◽  
The Individual

 In the last decades, individual urban traffic flows have increased all over the world with a consequent growth of road congestion and environmental pollution. In this context, car-pooling is an interesting car-based alternative to satisfy the individual mobility demand by optimizing the car loading factor with respect to the number of passengers, provided that all the participants share trip origin and destination in the same time slot. To make the system more appealing, this paper proposes an on-demand car-pooling service adopting variable fares, on the basis of trip length and number of participants. Multi-agent, reputation and recommender system technologies in synergy with a routing algorithm have been used to this aim. Experiments on simulated data proved the potentiality of the proposed approach.

scholarly journals Public Preferences and Willingness to Pay for Shared Autonomous Vehicles Services in Nagoya, Japan

Smart Cities ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 230-244 ◽  
Author(s):  
Mingyang Hao ◽  
Yanyan Li ◽  
Toshiyuki Yamamoto
Keyword(s):  
Willingness To Pay ◽  
Autonomous Vehicles ◽  
Stated Preference ◽  
Autonomous Vehicle ◽  
Transportation Mode ◽  
Public Preferences ◽  
Socioeconomic Characteristics ◽  
On Demand ◽  
Vehicle Systems ◽  
The Relationship

Shared autonomous vehicle systems are anticipated to offer cleaner, safer, and cheaper mobility services when autonomous vehicles are finally implemented on the roads. The evaluation of people’s intentions regarding shared autonomous vehicle services appears to be critical prior to the promotion of this emerging mobility on demand approach. Based on a stated preference survey in Nagoya, Japan, the preference for shared autonomous vehicle services as well as willingness to pay for these services were examined among 1036 respondents in order to understand the relationship between people’s socioeconomic characteristics and their preferred shared autonomous vehicle services. For this purpose, k-modes clustering technique was selected and six clusters were obtained. Six groups with respect to different interests on shared autonomous vehicle services were clustered. The result of correlation analysis and discussion of willingness to pay on services provided insightful results for the future shared autonomous vehicle services. This study not only aids in revealing the demands of customer different clusters, but also states the prospective needs of users for stakeholders from research, policymaker and industry field, who are preparing to work on promoting shared autonomous vehicle systems, and subsequently, develops an optimum transportation mode by considering both demand and services as a whole.

A Learning-Based Optimization Approach for Autonomous Ridesharing Platforms with Service-Level Contracts and On-Demand Hiring of Idle Vehicles

2021 ◽  
Author(s):  
Breno A. Beirigo ◽  
Frederik Schulte ◽  
Rudy R. Negenborn
Keyword(s):  
New York ◽  
Autonomous Vehicles ◽  
Transportation Network ◽  
Supply And Demand ◽  
Service Level ◽  
Third Party ◽  
Optimization Approach ◽  
Marginal Value ◽  
On Demand ◽  
Demand Fluctuations

Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Because of supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately because service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles, however, the characteristics of mobility services change and new opportunities to overcome the prevailing limitations arise. In this paper, we consider an autonomous ridesharing problem in which idle vehicles are hired on-demand in order to meet the service-level requirements of a heterogeneous user base. In the face of uncertain demand and idle vehicle supply, we propose a learning-based optimization approach that uses the dual variables of the underlying assignment problem to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. These approximations are used in the objective function of the optimization problem to dispatch, rebalance, and occasionally hire idle third-party vehicles in a high-resolution transportation network of Manhattan, New York City. The results show that the proposed policy outperforms a reactive optimization approach in a variety of vehicle availability scenarios while hiring fewer vehicles. Moreover, we demonstrate that mobility services can offer strict service-level contracts to different user groups featuring both delay and rejection penalties.

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