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Currently, there is a great need for new methods to collect travel data. Traditional methods have considerable drawbacks and, at the same time, the models used to analyse the transport system require more and more detailed and high-quality data. An alternative method that stands out as very promising is to capture raw data from devices that can use any positioning technology (e.g., GPS, WiFi positioning, GSM, etc.), followed by transforming the raw data into meaningful travel data. Since most smartphones are equipped with various sensors that can be used to determine the location of the smartphone, and since smartphones are integrated in the daily life of most people, they provide an unprecedented opportunity for large-scale travel data collection. This method has a great potential to solve the problems related to the estimation of distance/travel time, geographic coding of departure/destination locations and forgotten trips and it will also provide a more detailed and extensive data set. In a recently completed research project the feasibility of replacing or complementing the traditional travel diary, with a suite of tools that make use of smartphone collected travel data has been evaluated. The advantages and disadvantages of the traditional method and the proposed method were studied. For a fair comparison, both methods have been tested in the same city, at the same time, and with the same respondents. To achieve the objectives of the project, MEILI, a system that consists of a smartphone application for capturing the movement of users and a web application for allowing the users to annotate their movement, has been deployed. The recruitment of respondents is a critical phase for traditional travel diaries and, as expected, this was the case also for the smartphone based method. A lesson learnt was that it is important to simplify the registration process as much as possible. In total 2142 trips were collected and annotated by 171 users. 51 of the users annotated trips covering more than a week. The experiences from the field trial shows that a smartphone based travel diary collection is a very useful complement to traditional travel diary collection methods since it appeals to a different age group and collects more detailed travel data for a longer period. The main findings of the paper are that smartphone based data collection is feasible, that the algorithms to save battery work well and that trips of the same respondent vary considerably depending on day of the week.

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed.

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed.

The velocity based cell transmission model (CTM-v) is a discrete time dynamical model that mimics the evolution of the traffic velocity field on highways. In this paper the CTM-v model is used together with an ensemble Kalman filter (EnKF) for the purpose of velocity sensor data assimilation. We present a calibration framework for the CTM-v and EnKF. The framework consists of two separate phases. The first phase is the calibration of the parameters of the fundamental diagram and the second phase is the calibration of demand and filter parameters. Results from the calibrated model are presented for a highway stretch north of Stockholm, Sweden.

Roundabouts have become a more common type of intersection in Sweden over the last 30 years. In order to evaluate the roundabout level-of-service both analytical models and simulation models are being used. Analytical traffic models for intersections, such as the Swedish capacity model Capcal, has difficulties estimating the level-of-service of a roundabout if there are pedestrians and cyclists at crossings located close to the roundabout. It is well known that a crossing located after a roundabout exit can cause an up-stream blocking effect that affects the performance of the roundabout. But how the upstream blocking effect depends on the different flows of vehicles and pedestrians is not known. In this paper an existing analytical model by Rodegerdts and Blackwelder has been investigated and compared to simulations in VISSIM and measurements from Swedish roundabouts. The purpose of this investigation is to examine if the model by Rodegerdts and Blackwelder is suitable for implementing into existing analytical models such as Capcal. The results show that the model by Rodegerdts and Blackwelder can estimate if a capacity loss will occur, but the magnitude of this loss is more difficult to evaluate. The conclusion and recommendation is that the model by Rodegerdts and Blackwelder should be implemented into the Swedish capacity model Capcal. The model by Rodegerdts and Blackwelder is to be used as a warning system if the results in Capcal are too uncertain to use for analysis of the roundabout performance. © 2011 Published by Elsevier Ltd.