RIDESOURCING IN MANUFACTURING SITES: A FRAMEWORK AND CASE STUDY

With the recent innovations in transportation, ridesourcing services have been proliferating in many countries. There are increasing attempts to apply ridesourcing in the corporate context. Manufacturing companies now install the Industrial Internet of Things (IIOT) sensors to vehicles to obtain real-time data on the movement of goods and materials. Despite the massive amount of data accumulated, little attention has been paid to exploiting the data for vehicle fleet management (FM). This paper proposes an analytical framework to solve two FM problems: how to group organizational units for vehicle sharing and where to deploy the groups. The framework is then validated with a case study of a Korean shipbuilder. The results indicate that grouping departments with similar spatial patterns can reduce the current fleet

Reliable Taxi Ride Sharing System

Now a day?s everyone is using taxi for riding but when there is a need of taxi we have to wait for a long time so for reducing the efforts a taxi-sharing system is developed that accepts taxi passenger?s real-time ride requests sent from browser and schedules proper taxis to pick up them via ridesharing and monetary constraints. The monetary constraints provide benefits for both passengers and taxi drivers: passengers will not pay more compared with ride and distance and driver will get more profit from this. While this system is beneficial in case of environment such as saving energy consumption. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via browser. In this rider will send request and driver will get acknowledgment. A scheduling process is then performed to select a taxi that satisfies the request with minimum increase in travel distance. A ride request generator is developed in terms of the stochastic process modeling real ride requests learned from the data set. Tested on this platform with extensive experiments, this system demonstrates its efficiency and effectiveness

Mobility Ranking - Human Mobility Analysis using Ranking Measures

This work investigates the impact of ranking measures in the analysis of mobility network. We consider big datasets of GPS trajectories that allowed us to construct two different kinds of networks: the network of carpooling between car drivers, and the bipartite graph between drivers and visited locations. We show how an analysis based on ranking drivers and locations reveals interesting properties of these networks

Multiple marginality and the emergence of popular transport:‘Saloni’ taxi-tricycles in Abidjan, Ivory Coast

Popular transport is the most significant form of urban mobility in the majority urban world and will continue to play an important role even as cities around the world overhaul and upgrade their transport systems. Since January 2019 a new type of popular transport, taxi-tricycles locally named “salonis,” has taken root at the peripheries of Abidjan, Côte d’Ivoire. This article offers an initial description of this new mode of mobility, the service it offers, the labor force it draws on, the forms of regulation that govern it, the spatial practices it has engendered, and its implications for sustainable urban mobility. We show that this new transport option has evolved from within the existing norms and practices of the city’s existing popular transport sector. Arguing that salonis have emerged at the intersection of multiple overlapping marginality – spatial, infrastructural, socio-economic, legal, and regulatory – we contribute to multi-disciplinary conceptualization of urban margins as a site of infrastructural creation and the production of space. Based on our analysis, we posit three possible future trajectories for salonis: illegality, expulsion, and experimentation.Les transports populaires constituent la forme de mobilité urbaine la plus importante dans les villes des pays du sud et continueront à jouer un rôle important, même quand ces villes repensent et modernisent leurs systèmes de transport. Depuis janvier 2019, un nouveau type de transport populaire a pris racine en périphérie de la ville d'Abidjan en Côte d'Ivoire : les taxi-tricycles nommés localement « salonis ». Cet article présente une première description de ce nouveau mode de mobilité, les services qu’ils offrent, la main-d’œuvre qu’ils utilisent, les formes de réglementation qui la régissent, les pratiques spatiales qu’ils ont engendrés et ses implications pour la mobilité urbaine durable. Nous montrons que cette nouvelle option de transport a évolué à partir des normes et pratiques existantes du système du secteur des transports populaires existant de la ville. En soutenant que les salonis ont émergé à l'intersection de multiples genres de marginalités imbriquées (spatiale, infrastructurelle, socio-économique, juridique et réglementaire), notre contribution est une conceptualisation multidisciplinaire des marges urbaines en tant que site de création d'infrastructures et de production d'espace. Il propose trois trajectoires futures possibles pour les salonis : l’illégalité, l’expulsion et l’expérimentation.El transporte popular es una de las formas más importantes de movilidad urbana en la mayoría de los países del sur y seguirá desempeñando un importante rol, más allá que las ciudades de tales países mejoren sus sistemas de transporte. Desde enero del 2019, emergió en las periferias de Abidján, Costa de Marfil, un nuevo tipo de transporte popular, los taxis-triciclos localmente conocidos como “salonis". Este artículo propone una descripción de este nuevo modo de movilidad, el servicio ofrecido, fuerza laboral que involucra, regulación asociada, prácticas espaciales que ha desencadenado y sus consecuencias para la movilidad urbana sostenible. Mostramos que esta nueva opción de transporte ha evolucionado en el contexto de las normas y prácticas ya existentes del sector de transporte popular en la ciudad. Además, planteamos que los “salonis” han emergido en el contexto de la intersección de múltiples marginalidades superpuestas (espacial, infraestructura, socioeconómica, legal y regulatoria). De tal forma, este trabajo contribuye a la conceptualización multidisciplinaria del funcionamiento urbano de una parte de la ciudad, como un sitio de creación de infraestructura y producción del espacio, proponiendo a partir de nuestro análisis, tres trayectorias posibles para este medio de transporte: ilegalidad, expulsión y experimentación

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data