International Journal of Intelligent Transportation Systems Research 

About: International Journal of Intelligent Transportation Systems Research is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Traffic flow. It has an ISSN identifier of 1348-8503. Over the lifetime, 351 publications have been published receiving 2578 citations. The journal is also known as: International journal of intelligent transportation systems research (Print) & International journal of Intelligent Transport Systems research.

A Covering Tour Approach for Disaster Relief Locating and Routing with Fuzzy Demand

Abstract: Optimizing the distribution and allocation of resources among individuals is one of the most important measures to be taken at the time of crisis. Time, as a vital factor, has a significant impact on the increase in the number of people rescued by relief activities. This paper presents an allocation and routing model for relief vehicles in the areas affected by a disaster. It uses a covering tour approach to reduce response time. Moreover, because determining the exact amount of demand for essential goods in the event of a disaster is very difficult and even impossible in some cases, the demand parameter is considered as a fuzzy parameter in this model. Accordingly, an optimization method is designed based on credibility theory, and a harmony search algorithm with random simulation is developed. Finally, the efficiency of the harmony search algorithm is analyzed by comparing the CPLEX solver and GRASP algorithm. The results show that the proposed algorithm performs well over a short operating time.

Bluetooth in Intelligent Transportation Systems: A Survey

Abstract: The rise of Bluetooth-equipped devices in personal consumer electronics and in in-car systems has revealed the potential to develop Bluetooth sensor systems for applications in intelligent transportation systems These applications may include measurements of traffic presence, density, and flow, as well as longitudinal and comparative traffic analysis A basic Bluetooth sensor system for traffic monitoring consists of a Bluetooth probe device (s) that scans for other Bluetooth-enabled device (s) within its radio proximity, and then stores the data for future analysis and use The scanned devices are typically on-board vehicular electronics and consumer devices carried by the driver and/or passengers which use Bluetooth communications, and which then reasonably proxy for the vehicle itself This paper surveys the scope and evolution of these systems, with system attributes and design decisions illustrated via a reference design The work provides motivation for continued development of non-invasive systems that leverage the existing communication infrastructure and consumer devices that incorporate short range communication technology like Bluetooth

Detection of Driving Events using Sensory Data on Smartphone

Abstract: In a fast-paced environment of today society, safety issue related to driving is considered a second priority in contrast to travelling from one place to another in the shortest possible time. This often leads to possible accidents. In order to reduce road traffic accidents, one domain which requires to be focused on is driving behaviour. This paper proposes three algorithms which detect driving events using motion sensors embedded on a smartphone since it is easily accessible and widely available in the market. More importantly, the proposed algorithms classify whether or not these events are aggressive based on raw data from various on board sensors on a smartphone. In addition, one of the outstanding features of the proposed algorithm is the ability to fine tune and adjust its sensitivity level to suit any given application domain appropriately. Initial experimental results reveal that the pattern matching algorithm outperforms the rule-based algorithm for driving events in both lateral and longitudinal movements where a high percentage of detection rate has been obtained for 11 out of 12 types of driving events. In addition, a trade-off between the detection rate and false alarm rate has been demonstrated under a range of algorithm settings in order to illustrate the proposed algorithm’s flexibility.

Spatial-Temporal Daily Frequent Trip Pattern of Public Transport Passengers Using Smart Card Data

Abstract: Rebuilding the operation scheme of public transportation is a recent topic of discussion in Japan because the number of passengers is decreasing especially in rural areas. This research empirically analyzes variations in trip patterns to understand how passengers’ daily travel patterns vary temporally and spatially among one month using smart card data. The analysis reveals that the daily number of trips for the entire network does not vary significantly. However, it also reveals that daily frequent trip patters of individual passengers vary, i.e., most passengers are not traveling by a single trip pattern. This could be fundamental knowledge to discuss more detail or individual operation schemes.

A Multi-Modal Driver Fatigue and Distraction Assessment System

Abstract: In this paper, we present a multi-modal approach for driver fatigue and distraction detection. Based on a driving simulator platform equipped with several sensors, we have designed a framework to acquire sensor data, process and extract features related to fatigue and distraction. Ultimately the features from the different sources are fused to infer the driver’s state of inattention. In our work, we extract audio, color video, depth map, heart rate, and steering wheel and pedals positions. We then process the signals according to three modules, namely the vision module, audio module, and other signals module. The modules are independent from each other and can be enabled or disabled at any time. Each module extracts relevant features and, based on hidden Markov models, produces its own estimation of driver fatigue and distraction. Lastly, fusion is done using the output of each module, contextual information, and a Bayesian network. A dedicated Bayesian network was designed for both fatigue and distraction. The complementary information extracted from all the mod- ules allows a reliable estimation of driver inattention. Our experimental results show that we are able to detect fatigue with 98.4 % accuracy and distraction with 90.5 %.