Gamification of telematics data to enhance operators’ behaviour for improvement of machine productivity in loading cycles

Construction industry is suffering from low productivity rate in various projects such as excavation. Although this issue is discussed in literature and several approaches are proposed to address it, productivity rate is still low in construction industry compared to other domains like manufacturing. Three core components directly affect the overall productivity in construction sector, i.e. labour productivity, raw material productivity, and machine or equipment productivity. With a focus on construction machinery, three factors influence productivity at excavation sites; i.e. 1) machine-based productivity and its configuration, 2) site layout and environmental conditions, and 3) operators’ behaviour. Operators’ competence and motivation represent two key parameters that affect their behaviour. On one side, gamification has attracted a growing area of interest both in literature and practice, seeking to place a layer of entertainment and pleasure to the top of serious activities (with a focus on improving the applicant’s motivation and behaviour). On the other side, telematics systems are utilized to collect operational data of the machine, and calculate its productivity rate. Telematics data are presented to operators (via a built-in screen available in the cabin of the machine) to provide real-time feedback about machine performance. In addition, these data can support machine owners to perceive operators’ behaviour on a real-time basis. To conclude, telematics systems are providing real-time data which can be a great input into gamification. A guideline is proposed in this dissertation that helps gamification designers to develop more transparent gamification models. This guideline is utilized to introduce a gamification model that gamifies telematics data with a focus on enhancing operators’ behaviour (machine productivity) in loading and transferring activities. The model was implemented at two sites(one recycling and one mining site) and could encourage operators (who were operating wheel-loaders and dump-trucks) to prevent redundant activities like texting, phoning, and even eating while operating the machine. Subsequently, it enhanced overall machine productivity up to 37% during the site observation. To summarize, a gamified platform in which different operators from different organizations can share their achievements, or can get scored and ranked in a leader-board will potentially lead to a more proper operators’ behaviour at work and subsequently can improve overall productivity rate at construction sites

Gamification of telematics data to enhance operators’ behaviour for improvement of machine productivity in loading cycles

Construction industry is suffering from low productivity rate in various projects such as excavation. Although this issue is discussed in literature and several approaches are proposed to address it, productivity rate is still low in construction industry compared to other domains like manufacturing. A gamified platform in which different operators from different organizations can share their achievements, or can get scored and ranked in a leader-board will potentially address this issue

The design and implementation of serious games for driving and mobility

The automotive and transportation sectors are showing consistent improvements in trends and standards concerning the safe and convenient travel of the road users. In this growing community of road users, the driver performance is a notable factor as many on-road mishaps emerge out of poor driver performance. In this research work, a case-study and experimental analysis were conducted to improve driver performance through the deployment of serious games. The primary motive of this work is to stimulate the on-road user performance through immediate feedback, driver coaching, and real-time gamification methods. The games exploit the cloud-based architecture to retrieve the driver performance scores based on real-time evaluation of vehicle signals and display the outcomes on game scene by reflecting the game parameters based on real-world user performance (in the context of driving and mobility). The deployment of games in cars is the topic of interest in current state-of-the-art, as there are more factors associated with it, such as safety, usability, and willingness of the users. These aspects were taken into careful consideration while designing the paradigm of gamification model. The user feedback for the real-time games was extracted through pilot tests and field tests in Genova. The gamification and driver coaching aspects were tested on various occasions (plug-in and field tests conducted at 5 European test sites), and the inputs from these field tests enabled to tune the parameters concerning the evaluation and gamification models. The improvement of user behavior was performed through a virtuous cycle with the integration of virtual sensors to the serious gaming framework. As the culmination, the usability tests for the real-time games were conducted with 18 test users to understand the user acceptance criteria and the parameters (ease of use and safety) that would contribute to the deployment of games. Other salient factors such as the impact of games, large-scale deployment, collaborative gaming and exploitation of gaming framework for 3rd party applications were also investigated in this research activity. The analysis of the usability tests states that the user acceptance of the implemented games is good. The report from usability study has addressed the user preferences in games such as duration, strategy and gameplay mechanism; these factors contribute a foundation for future research in implementing the games for mobility

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

Optimizing city-scale traffic through modeling observations of vehicle movements

The capability of traffic-information systems to sense the movement of millions of users and offer trip plans through mobile phones has enabled a new way of optimizing city traffic dynamics, turning transportation big data into insights and actions in a closed-loop and evaluating this approach in the real world. Existing research has applied dynamic Bayesian networks and deep neural networks to make traffic predictions from floating car data, utilized dynamic programming and simulation approaches to identify how people normally travel with dynamic traffic assignment for policy research, and introduced Markov decision processes and reinforcement learning to optimally control traffic signals. However, none of these works utilized floating car data to suggest departure times and route choices in order to optimize city traffic dynamics. In this paper, we present a study showing that floating car data can lead to lower average trip time, higher on-time arrival ratio, and higher Charypar-Nagel score compared with how people normally travel. The study is based on optimizing a partially observable discrete-time decision process and is evaluated in one synthesized scenario, one partly synthesized scenario, and three real-world scenarios. This study points to the potential of a "living lab" approach where we learn, predict, and optimize behaviors in the real world

A Methodological Framework to Assess Road Infrastructure Safety and Performance Efficiency in the Transition toward Cooperative Driving

There is increasing interest in connected and automated vehicles (CAVs), since their implementation will transform the nature of transportation and promote social and economic change. Transition toward cooperative driving still requires the understanding of some key questions to assess the performances of CAVs and human-driven vehicles on roundabouts and to properly balance road safety and traffic efficiency requirements. In this view, this paper proposes a simulation-based methodological framework aiming to assess the presence of increasing proportions of CAVs on roundabouts operating at a high-capacity utilization level. A roundabout was identified in Palermo City, Italy, and built in Aimsun (version 20) to describe the stepwise methodology. The CAV-based curves of capacity by entry mechanism were developed and then used as target capacities. To calibrate the model parameters, the capacity curves were compared with the capacity data simulated by Aimsun. The impact on the safety and performance efficiency of a lane dedicated to CAVs was also examined using surrogate measures of safety. The paper ends with highlighting a general improvement with CAVs on roundabouts, and with providing some insights to assess the advantages of the automated and connected driving technologies in transitioning to smarter mobilit

Improved Low Cost GPS Localization By Using Communicative Vehicles

International audienceIn this paper, a new collaborative localization method is proposed. On the assumption that the distance between two communicative vehicles can be calculated with a good precision, cooperative vehicle are considered as additional satellites into the user position calculation by using iterative methods. In order to limit divergence, some filtering process is proposed: Interacting Multiple Model (IMM) is used to guarantee a greater robustness in the user position estimation

Integrating Multiple Alarms & Driver Situation Awareness

This study addresses this gap in CAS and intelligent alarm research by examining whether or not a single master alarm warning versus multiple warnings for the different collision warning systems conveys adequate information to the drivers. Intelligent driver warning systems signaling impending frontal and rear collisions, as well as unintentional lane departures were used in this experiment, and all the warnings were presented to drivers through the auditory channel only. We investigated two critical research questions in this study: 1. Do multiple intelligent alarms as opposed to a single master alarm affect drivers’ recognition, performance, and action when they experience a likely imminent collision and unintentional lane departure? 2. Is driver performance and overall situation awareness under the two different alarm alerting schemes affected by reliabilities of the warning systems?Prepared For Ford Motor Compan

Development of rear-end collision avoidance in automobiles

The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor