Abstract
Walking is the original form of transportation, and pedestrians have always made up a significant share of transportation system users. In contrast to motorized traffic, which has to move on precisely defined lanes and follow strict rules, pedestrian traffic is not heavily regulated. Moreover, pedestrians have specific characteristics—in terms of size and protection—which make them much more vulnerable than drivers. In addition, the difference in speed between pedestrians and motorized vehicles increases their vulnerability. All these characteristics, together with the large number of pedestrians on the road, lead to many safety problems that professionals have to deal with. One way to tackle them is to model pedestrian behavior using microsimulation tools. Of course, modeling also raises questions of reliability, and this is also the focus of this paper. The aim of the present research is to contribute to improving the reliability of microsimulation models for pedestrians by testing the possibility of applying neural networks in the model calibration process. Pedestrian behavior is culturally conditioned and the adaptation of the model to local specifics in the calibration process is a prerequisite for realistic modeling results. A neural network is formulated, trained and validated in order to link not-directly measurable model parameters to pedestrian crossing time, which is given as output by the microsimulation tool. The crossing time of pedestrians passing the road on a roundabout entry leg has been both simulated and calculated by the network, and the results were compared. A correlation of 94% was achieved after both training and validation steps. Finally, tests were performed to identify the main parameters that influence the estimated crossing time.
Highlights
In a world where urbanization is increasing—statistics predict a 58% growth in the world’s urban population by 2025 [1] and where all communities are tending towards more sustainable and environmentally friendly transport policies, it is understandable that pedestrians play a central role in the urban environment
The aim of this paper is to show the applicability of the neural network to the prediction of the microsimulation output, giving the opportunity to effectively use it in the calibration process under development
38% of the data have a Mean Absolute Error (MAE) lower than 5% and 21% have a MAE between 5% and 10%, for a total amount of 59% of data with a MAE lower than 10%, which is an acceptable threshold for the first training, test and validation of the network
Summary
In a world where urbanization is increasing—statistics predict a 58% growth in the world’s urban population by 2025 [1] and where all communities are tending towards more sustainable and environmentally friendly transport policies, it is understandable that pedestrians play a central role in the urban environment. Many transportation problems could be minimized and in some cases completely solved by proper planning of the urban environment: The development of walkable spaces could reduce the problems of congestion in city centers and pollution could decrease if people prefer to walk rather than using other means of transport. A well-known and sophisticated tool for planning and design of transportation infrastructures is microsimulation. This technique is already widely used in transportation engineering and has many advantages; in particular it provides the possibility of studying various infrastructural options without realizing them, in order to better meet the needs of the considered road users, may they be Levels Of Service (LOS), emergency needs or safety-related issues. To be useful, it is necessary to verify the accuracy and reliability of the developed models and the obtained results have to be checked: calibration and validation steps are necessary to be run, processes that request a long elaboration period and several efforts [2,3]
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