Title: Modelling car driver informing and route choice under uncertainty about DRIP travel times
Abstract: Travel and traffic information play an important role in traveller behaviour and traffic flows. Travellers have the possibility to use information on current and future traffic situations to make their travel decisions. Travel decisions by individual travellers subsequently influence the performance of a total traffic system. This makes behaviour of travellers an interesting topic to policy makers and road operators. In this thesis travel choice behaviour also plays a central role and we specifically take into account car drivers and en-route route choice. This graduation assignment was done at TNO and after an initial scope further focus was applied resulting in the goal of modelling driver traffic information use and subsequent enroute choice. After coupling en-route route choice to DRIPs as the channel for travel time information in this study it was decided to model in the ITS Modeller. Latter provides a micro simulation environment through which driver behaviour can be analyzed at the individual driver level. First opportunities for improvement in modelled driver use of information during en-route route choice were identified. The following research questions were used to further explore and implement a driver information model. RQ1 How can the DRIP information market be characterized and how do market dynamics affect driver route choice behaviour? Through a concise exploration the DRIP information market and visit to the Verkeerscentrale Midden-Nederland we made some interesting findings. These findings originate from the fact that the DRIP information chain is a complex system involving many technical systems but also a multitude of stakeholders. Some examples resulting from DRIP information dynamics are used to illustrate effects on route choice behaviour. The first example is formed by occasional manual operation of DRIPs by traffic controllers. During this process it appears information provisioning to drivers is prone to delays. Secondly it became clear that the small number of responsible parties for maintenance of DRIP control systems posses a large amount of knowledge seemingly necessary to maintain DRIP information quality. Both examples carry a risk of information quality deteriation and as such a the risk of losing trust of drivers in DRIP information. RQ2 Given the lack of a driver information evaluation mechanism in the ITS Modeller: How can driver route choice behaviour be modelled and implemented? This report identifies an apparent opportunity for improving driver route choice modelling under influence of DRIP travel time information. A driver information model is proposed in which car drivers exhibit updating behaviour on their travel time estimations and uncertainty about these times. The model fits in the current method of modelling route choice in the ITS Modeller. Our driver model is based on uncertainty about a drivers’ own mean travel time estimation and travel times provided by a DRIP. High uncertainty about his own estimation leads a car driver to update his expected travel time closer to that predicted by a DRIP. If the opposite is true then a driver updates his expected travel time closer to his initial estimation. In other words: “Updated quality perceptions are a weighted average of prior beliefs and observed quality. Weights reflect perceived reliability of prior beliefs and observations, respectively: when the traveller distrusts (trusts) his own observations, updated perceptions of quality are relatively close to initially anticipated quality (observed quality)” (Chorus & Dellaert, Forthcoming) RQ3 What are the route choice effects of the proposed driver informing model? The simulation phase illustrates the difference in driver travel time updating for different levels of uncertainty while all other simulation factors are kept constant. The implemented driver model indeed leads to different updated route cost under influence of travel time information. In the particular scenario comparison overall network effects due to difference in route choice behaviour were minimal though because of the small absolute difference in updated route costs. Further face validity analysis indicates that this small change in route choice behaviour early in the peak period appears to have a positive effect on travel time dispersion. This conclusion is not supported by significance and as such is made with reservation. Based on this study we recommend the following for further research: Practical recommendations: Inclusion of truck drivers in use of the driver information model and future research. In this study we assume the group of drivers only consist of car drivers. In practise this is not the case and this study indicated that truck drivers show different route choice behaviour in light of traffic information, specifically route and travel time information through a DRIP. The current study overestimates the effect of provided information. The utility function in our study consists of travel time and travel time uncertainty. In practise more factors may be important in a drivers’ en-route route choice consideration. Further research should investigate which factors are important. Practical recommendations: During implementation of the model it appeared that an implementation at individual driver level would lead to too high computation times. We consider some opportunities for better implementation to be feasible. TNO has the opportunity to use server based computation. Running the improved driver information model in such an environment might result in acceptable analysis times.An other option is further evolution of the way in which the driver information model is applied to drivers. An implementation could be conducted analogous to current implementation of for example lane changing and car following models. We assume the lack of influence of a DRIP on network familiarity. In short route information of a DRIP does not lead to inclusion of a route in a drivers’ route set. Further improvement of the driver information model ought to take inclusion into account. We saw that in certain situations DRIP information delays occur outside of control of the CDMS (Centraal DRIP Management Systeem). It is deemed beneficial if the source of this delay, manual operation, is taken into consideration and how this affects DRIP information delay. Gained insights could be used to have a future calibrated driver information model more robust.
Publication Year: 2011
Publication Date: 2011-08-31
Language: en
Type: article
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