Application of the Combination of Random Utility Models (CoRUM) to route choice

Papola, Andrea; Tinessa, Fiore; Marzano, Vittorio

doi:10.1016/j.trb.2018.03.014

Cited by 34 publications

(12 citation statements)

References 33 publications

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“…In transportation engineering, analytical methods have been applied to address traffic congestion problems by making efforts to shift demand from private vehicles to transit systems [1] and to put into operation better road traffic control implementing tools, such as traffic simulation [2][3][4][5][6][7], network dynamic equilibrium models [8][9][10], and other methodologies that are devoted to change user route choice [11][12][13][14][15]. Analytical methods based on traffic network simulation have rarely been implemented in order to produce an evaluation of the level of safety of a given traffic scenario.…”

Section: Introductionmentioning

confidence: 99%

Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data

et al. 2020

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The traditional analysis of road safety is based on statistical methods that are applied to crash databases to understand the significance of geometrical and traffic features on safety, or in order to localize black spots. These classic methodologies, which are based on real crash data and have a solid background, usually do not explicitly consider the trajectories of vehicles at any given location. Moreover, they are not easily applicable for making comparisons between different traffic network designs. Surrogate safety measures, instead, may enable researchers and practitioners to overcome these limitations. Unfortunately, the most commonly used surrogate safety measures also present certain limits: Many of them do not take into account the severity of a potential collision and the dangers posed by road-side objects and/or the possibility of drivers being involved in a single-vehicle crash. This paper proposes a new surrogate safety indicator founded on vehicle trajectories, capable also of considering road-side objects. The validity of the proposed indicator is assessed by means of comparison between the calculation of surrogate safety measures on micro-simulated trajectories and the real crash risk obtained with data on real crashes observed at several urban intersection scenarios. The proposed experimental framework is also applied (for comparison) to classical indicators such as TTC (time to collision) and PET (post-encroachment time).

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Section: Introductionmentioning

confidence: 99%

Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data

et al. 2020

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“…Connected vehicles may help to solve traffic congestion problems which were hardly solved with traditional methodologies. Traditional traffic engineering practice was based on attempts to shift demand on transit systems [2] and on better road traffic control by adopting tools such as: traffic simulation [3][4][5][6][7][8] dynamic network loading equilibrium and dynamic models [9][10][11][12] and the study and attempt to affect user route choice [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Floating Car Data Adaptive Traffic Signals: A Description of the First Real-Time Experiment with “Connected” Vehicles

et al. 2020

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The future of traffic management will be based on “connected” and “autonomous” vehicles. With connected vehicles it is possible to gather real-time information. The main potential application of this information is in real-time adaptive traffic signal control. Despite the feasibility of using Floating Car Data (FCD), for signal control, there have been practically no real experiments with all “connected” vehicles to regulate traffic signals in real-time. Most of the research in this field has been carried out with simulations. The purpose of this study is to present a dedicated system that was implemented in the first experiment of an FCD-based adaptive traffic signal. For the first time in the history of traffic management, a traffic signal has been regulated in real time with real “connected” vehicles. This paper describes the entire path of software and system development that has allowed us to make the steps from just simulation test to a real on-field implementation. Results of the experiments carried out with the presented system prove the feasibility of FCD adaptive traffic signals with commonly-used technologies and also establishes a test-bed that may help others to develop better regulation algorithms for these kinds of new “connected” intersections.

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“…Traditionally in transportation engineering, quantitative approaches have been applied to solve congestion problems with attempts to shift demand on transit systems [26] and to implement better road traffic control by adopting tools such as traffic simulation [18,[27][28][29][30][31] dynamic network loading equilibrium, dynamic models [32][33][34][35] and methodologies attempting to affect user route choice [36][37][38][39][40]. Quantitative approaches involving network modelling have seldom been applied to Traffic Safety evaluation.…”

Section: Introductionmentioning

confidence: 99%

Validation of Simulated Safety Indicators with Traffic Crash Data

et al. 2020

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The purpose of this document is to validate a new methodology useful for the estimation of road accidents resulting from possible driver distractions. This was possible through a statistical comparison made between real accident data between 2016 and 2018 in the city of Santander (Spain) and simulated data resulting from the application of the methodology on two areas of study. The methodology allows us to evaluate possible collisions starting from the knowledge of vehicular trajectories extrapolated from microsimulation. Studies show that there are good correlations between the real data and the simulated data. The results obtained show that the proposed methodology can be considered reliable and, therefore, it could be of fundamental importance for designers, since it would simplify the choice between different possible intervention scenarios, determining which is the least risky in terms of road safety.

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Application of the Combination of Random Utility Models (CoRUM) to route choice

Cited by 34 publications

References 33 publications

Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data

Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data

Floating Car Data Adaptive Traffic Signals: A Description of the First Real-Time Experiment with “Connected” Vehicles

Validation of Simulated Safety Indicators with Traffic Crash Data

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