Hybrid Vehicle-Centric Route Guidance 1

Farver, J.; Chabini, Ismaïl

doi:10.1016/s1474-6670(17)32411-4

Cited by 14 publications

(4 citation statements)

References 46 publications

(80 reference statements)

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“…For the microscopic scale a hybrid centralised/ decentralised solution is suggested in [18,19]. However, in the first case the small-scale components are mostly used for incident detection and rerouting and no coupling is made with traffic light controllers, whereas the second case only provides a development framework which needs to be further extended.…”

Section: Introductionmentioning

confidence: 99%

Micro-routing using accurate traffic predictions

Blokpoel¹,

Vreeswijk²,

Leistner

2012

IET Intelligent Transport Systems

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This study presents a new way of routing vehicles through a network. Current routing algorithms base their advice on measurements of probe vehicles and sometimes do predictions based on historical data. However, if all vehicles would follow up that advice, those predictions would not be valid anymore. As a result, oscillations could arise, in turn causing traffic jams. The micro-routing algorithm presented in this study takes previous advice into account for the next advice and is able to interact with traffic light control programmes. In simulation, a reduction of travel time up to 30% and a reduction in the number of stops up to 46% were achieved without any oscillations. This shows high potential for solving congestion and reducing CO 2 emissions. Furthermore, a brief review of literature on route choice behaviour and driver response to route advice gives the reader some insight into driver compliance factors. As the major requirements of high reliability, predicted information and prescriptive information are met, also from a compliance perspective the micro-routing algorithm is expected to be effective.

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Section: Introductionmentioning

confidence: 99%

Micro-routing using accurate traffic predictions

Blokpoel¹,

Vreeswijk²,

Leistner

2012

IET Intelligent Transport Systems

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“…Due to their drawbacks none of those route guidance methods has prevailed so far and new architectures, such as the Hybrid route guidance, have emerged to integrate those methods and use their strengths. For example, in a Hybrid route guidance system the predictive guidance is generated in a centralized layer and revised in a decentralized layer (for a comprehensive description see [86] [86]: Farver (2005), 'Hybrid vehiclecentric route guidance'…”

Section: Route Guidance Systems For In-vehicle and Mobile Navigation mentioning

confidence: 99%

Dynamic transport mode estimation subject to joint decisions and spatio-temporal variations

Gkiotsalitis¹

2019

Preprint

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This thesis studies the dynamic route and transport mode optimization for participating in a jointly decided activity subject to spatio-temporal variations. Joint activity participants have to travel from their current locations to a common location which is the location of the joint activity. Apart from the recurrent joint activities (such as work, school etc.), there are several joint leisure activities where activity participants have to decide about the location and the starting time of the joint leisure activity along with the transport mode(s) that each one of them has to use for commuting from his/her current location to the joint leisure activity location.The objective of this thesis is the development of a comprehensive system for the optimization of joint leisure trips that are not related to work by optimizing (a) the location and the starting time of the joint leisure activity (b) the public transport operations (c) the transport mode(s) selection for each activity participant in order to arrive there as fast as possible while satisfying his/her personal trip preferences.Activities not related to work can be responsible for more than 60\% of trips at an urban environment. Non-working travel patterns differ from the more stable, recurrent travel patterns of work-related activities such as trips from/to work, school etc. and the three main differences of those activities are: (1) The location of a leisure activity can differ on a daily basis (it is not static like the location of the working or studying place); (2) The starting time of a leisure activity has greater elasticity and can differ on a daily basis (it is not stable such as the starting time of work, school etc.); (3) The alternative journey options to and from a leisure activity location are not well-known to the users (users are more aware of their journey alternatives when it comes to transfers from/to work-related activities since those activities are re-current) . Given that a significant number of transfers is related to leisure activities, the optimization of the (1) location selection, (2) starting activity time, (3) transport mode selection and (4) route selection are of paramount importance for both the commuters' total travel cost and the transport network performance. In addition, the prediction of non-recurrent activities in time and space can be an important step forward for the tactical and dynamic planning of transport networks since the volume and the non-recurrent nature of such activities lead to significant travel demand variations compared to the more stable, work-related activities. Due to the above, this thesis focuses on: (i) Understanding the State-of-the-Art (SoA) work on utilizing user-generated data for increasing the efficiency level of joint leisure activities and proposing actions towards this direction; (ii) Capturing users' willingness to travel certain distances for participating in different types of activities; (iii) Optimizing the selection of locations and starting times of joint leisure activities (iv) Re-scheduling the starting times of public transportation trips in order to adjust to the joint leisure activity demand without deteriorating the Quality of Service (QoS) for other passengers; (v) Optimizing the journey/path selection of users' who are willing to travel from one point of the network to another for participating at one activity and, possibly, utilize multiple modes while also satisfying their preferences.

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“…Farver (13) proposed a new concept called vehicle-centric route guidance to enhance the quality of route guidance strategies. She developed a mesoscopic simulator to test the proposed algorithm with comprehensive experiments on a hypothetical network consisting of four origin-destination (O-D) pairs.…”

Section: Route Guidancementioning

confidence: 99%

Evaluation of Route Guidance Strategies Based on Vehicle-Infrastructure Integration under Incident Conditions

Lee

Park

2008

Transportation Research Record

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As an individual driver, having a route guidance system that provides the shortest path based on distance or an alternative path during an incident condition can be extremely useful. Even though it is more desirable to use travel times in providing route guidance, they are not readily available for every roadway segment. With the Vehicle Infrastructure Integration (VII) initiative, it is anticipated in the near future that individual vehicles equipped with VII could collect travel times and transfer such information to other vehicles and the transportation management system. Thus, travel-time information for real-time route guidance could be available through the VII. System-level potentials of the VII were explored by evaluating various route guidance strategies within the VII environment. A simulation framework was developed representing the VII-enabled virtual world, and route guidance strategies were evaluated with various factors, including market penetration of VII-equipped vehicles, congestion levels of a road network, update intervals of route guidance information, drivers’ compliance rates, and so forth. VII-based route guidance significantly reduced travel time over the no-guidance case. Among four route guidance strategies, averaged link travel time-based guidance produced the best performance. Other key findings included the following: higher market penetration generates bigger benefits, multiple operations of competing route guidance strategies did not degrade the networkwide performance, and prediction travel time-based guidance worked well for highly congested conditions.

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Hybrid Vehicle-Centric Route Guidance 1

Cited by 14 publications

References 46 publications

Micro-routing using accurate traffic predictions

Micro-routing using accurate traffic predictions

Dynamic transport mode estimation subject to joint decisions and spatio-temporal variations

Evaluation of Route Guidance Strategies Based on Vehicle-Infrastructure Integration under Incident Conditions

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