On M2M Micropayments : A Case Study of Electric Autonomous Vehicles

The proliferation of electric vehicles has spurred the research interest in technologies associated with it, for instance, batteries, and charging mechanisms. Moreover, the recent advancements in autonomous cars also encourage the enabling technologies to integrate and provide holistic applications. To this end, one key requirement for electric vehicles is to have an efficient, secure, and scalable infrastructure and framework for charging, billing, and auditing. However, the current manual charging systems for EVs may not be applicable to the autonomous cars that demand new, automatic, secure, efficient, and scalable billing and auditing mechanism. Owing to the distributed systems such as blockchain technology, in this paper, we propose a new charging and billing mechanism for electric vehicles that charge their batteries in a charging-on-the-move fashion. To meet the requirements of billing in electric vehicles, we leverage distributed ledger technology (DLT), a distributed peer-to-peer technology for micro-transactions. Our proof-of-concept implementation of the billing framework demonstrates the feasibility of such system in electric vehicles. It is also worth noting that the solution can easily be extended to the electric autonomous cars (EACs)

A Tabu Search Based Metaheuristic for Dynamic Carpooling Optimization

International audienceThe carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France

Research and innovation in smart mobility and services in Europe: An assessment based on the Transport Research and Innovation Monitoring and Information System (TRIMIS)

For smart mobility to be cost-efficient and ready for future needs, adequate research and innovation (R&I) in this field is necessary. This report provides a comprehensive analysis of R&I in smart mobility and services in Europe. The assessment follows the methodology developed by the European Commission’s Transport Research and Innovation Monitoring and Information System (TRIMIS). The report critically assesses research by thematic area and technologies, highlighting recent developments and future needs.JRC.C.4-Sustainable Transpor

EVALUATING THE SUSTAINABILITY IMPACTS OF INTELLIGENT CARPOOLING SYSTEMS FOR SOV COMMUTERS IN THE ATLANTA REGION

Community-based carpooling has more potential to help alleviate traffic congestion and reduce energy use during peak hours than ride-hailing services, such as Uber or Lyft, because community-based carpooling avoids deadheading operations. However, community-based carpooling is not fully exploited due to communication, demographic, and economic barriers. This thesis proposes a top-down computation framework to estimate the potential market-share of community-based carpooling, given the outputs of activity-based travel demand models. Given disaggregate records of commute trips, the framework tries to estimate a reasonable percentage/number of trips among commuters in single-occupancy vehicles (SOV) that can carpool together, considering spatiotemporal constraints of their trips. The framework consists of two major procedures: (1) trip clustering; and (2) trip optimization. The framework tackles the problems associated with using large amounts of data (for example, the Atlanta travel demand model predicts more than 19 million vehicle trips per day) by following “split-apply-combine” procedures. A number of tricks and technologies (e.g., pre-computing, databases, concurrency, etc.) are employed to make the mass computing tasks solvable in a personal laptop in a reasonable time. Two different methods are established to solve the carpooling optimization problem. One method is based on the bipartite algorithm, while the other uses integer linear programming. The linear programming method estimates both the systemic optimal performance in terms of saving the most vehicular travel mileage, while the bipartite-based algorithm estimates one Pareto optimal performance of such system that pairs the greatest number of carpool members (i.e., maximum number of travelers that can use the system) given acceptable (defined by the user) reroute cost and travel delays. The performance of these two methods are carefully compared. A set of experiments are run to evaluate the carpooling potentials among single-occupancy vehicles based on the output of activity-based model’s (ARC ABM) home-to-work single-occupancy vehicle (SOV) trips that can be paired together towards designated regional employment centers. The experiment showed that under strict assumptions, an upper bound of around 13.6% of such trips can be carpooled together. The distribution of these trips over space, time, and travel network are thoroughly discussed. The results are promising in terms of finding carpooling and decreasing total vehicle mileage. Moreover, the framework is flexible enough with the potential to act as a simulation testbed, to optimize vehicular operations, and to match potential carpool partners in real-time.M.S

Quantifying the Effects of Sustainable Urban Mobility Plans

This technical note uses the expert scoring information available in current scientific literature in order to explore the impacts and effects that different urban measures may have in planning for sustainability on a European wide level.JRC.J.1-Economics of Climate Change, Energy and Transpor

ROLE OF TAXI SUBSIDY SCHEME AS PUBLIC SERVICE FOR MOBILITY OF ELDERLY PEOPLE IN RURAL AREAS OF JAPAN

Public transportation in rural areas faces increasing challenges with increasingly aging populations. The elderly and disabled people who cannot drive by themselves highly rely on public transport for traveling. Therefore, to ensure the mobility needs for individual door-to-door services in depopulated areas with dispersed populations, several local authorities in Japan are implementing the taxi subsidy scheme (TSS) for the elderly. However, during the implementation, many issues relating to this policy, such as subsidy amount, usage time and number of distributed tickets, settings for target persons, and target area have been encountered. Based on this fact, we examined TSS from three perspectives: the local government that supports the policy with subsidy; small- and medium-sized taxi operators whose business management is influenced by TSS; and the elderly people with their outing status and TSS usage status. Furthermore, based on the trends of national policies, we examined the effective utilization of TSS as a public mobility service for the elderly in rural areas. As a result, for local governments, the TSS was found to be widely known as a support for vulnerable groups and for those who have returned their licenses, and the burden on residents is often not a large expense. From the viewpoint of taxi operators, TSS has considerably contributed to business management. Additionally, many business operators want to increase the usage time and number of people eligible for subsidies. The elderly survey showed that TSS users use taxi for various purposes and are less likely to be influenced by high prices compared with non-TSS users. In other words, it is suggested that the TSS should be the “ideal public transportation” by narrowing down the target users and improving the service

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Ride-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation

A passenger-to-driver matching model for commuter carpooling: Case study and sensitivity analysis

For the transport sector, promoting carpooling to private car users could be an effective strategy over reducing vehicle kilometers traveled. Theoretical studies have verified that carpooling is not only beneficial to drivers and passengers but also to the environment. Nevertheless, despite carpooling having a huge potential market in car commuters, it is not widely used in practice worldwide. In this paper, we develop a passenger-to-driver matching model based on the characteristics of a private-car based carpooling service, and propose an estimation method for time-based costs as well as the psychological costs of carpooling trips, taking into account the potential motivations and preferences of potential carpoolers. We test the model using commuting data for the Greater London from the UK Census 2011 and travel-time data from Uber. We investigate the service sensitivity to varying carpooling participant rates and fee-sharing ratios with the aim of improving matching performance at least cost. Finally, to illustrate how our matching model might be used, we test some practical carpooling promotion instruments. We found that higher participant role flexibility in the system can improve matching performance significantly. Encouraging commuters to walk helps form more carpooling trips and further reduces carbon emissions. Different fee-sharing ratios can influence matching performance, hence determination of optimal pricing should be based on the specific matching model and its cost parameters. Disincentives like parking charges and congestion charges seem to have a greater effect on carpooling choice than incentives like preferential parking and subsidies. The proposed model and associated findings provide valuable insights for designing an effective matching system and incentive scheme for carpooling services in practice

Implementation of the workplace parking levy as a transport policy instrument

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