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Data on travel patterns and travel demand are an important input to today's traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality's existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak ( R 2 < 0.2) using the original zoning used in the travel demand model with 189 zones, while it is significant with R 2 = 0.82 when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find systematic differences in the resulting travel demand matrices using two different methods. [ABSTRACT FROM AUTHOR]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem. Abbreviations APR: average penetration rate; DDNA: data driven network assignment; DDNL: data driven network loading; DODME: dynamic OD matrix estimation; DTA:dynamic traffic assignment; FCD: floating-car data; ITS: intelligent transportationsystems; NNLSQ: non-negative least squares; OD: origin destination; ODME: ODmatrix estimation; PV: probe vehicle; RUM: random utility model [ABSTRACT FROM AUTHOR]

As congestion pricing has moved from theoretical ideas in the literature to real-world implementation, the need for decision support when designing pricing schemes has become evident. This paper deals with the problem of finding optimal toll levels and locations in a road traffic network and presents a case study of Stockholm. The optimisation problem of finding optimal toll levels, given a predetermined cordon, and the problem of finding both optimal toll locations and levels are presented, and previously developed heuristics are used for solving these problems. For the Stockholm case study, the possible welfare gains of optimising toll levels in the current cordon and optimising both toll locations and their corresponding toll levels are evaluated. It is shown that by tuning the toll levels in the current congestion pricing cordon used in Stockholm, the welfare gain can be increased significantly, and furthermore improved by allowing a toll on a major bypass highway. It is also shown that, by optimising both toll locations and levels, a congestion pricing scheme with welfare gain close to what can be achieved by marginal social cost pricing can be designed with tolls being located on only a quarter of the tollable links. [ABSTRACT FROM AUTHOR]

We present a column generation procedure for the side constrained traffic equilibrium problem. A dual stabilization scheme is introduced to improve the computational performance. Computational experiments for the case of linear side constraints are presented. The test problems are well known traffic equilibrium instances where side constraints of link flow capacity type and general linear side constraints are added. The computational results are promising especially for instances with a relatively small number of side constraints. [Copyright &y& Elsevier]

This study presents a transport model to better model cycling demand. The model improves modelling of cycling in several ways compared to a conventional transport model. First, it uses a detailed bicycle network containing information about existing bicycle infrastructure. Second, generalised cost measures based on different bicycle route choice models are calculated and compared to evaluate how to best represent the impact of bicycle infrastructure in the model. Third, the model utilizes a refined zone system with smaller zones of size 250 m × 250 m. Using these smaller zones, more short-distance tours are included in the model, and these are predominantly walking and cycling trips. Fourth, the model considers cycling also as an access mode choice to public transport. Therefore, the model treats cycling and public transport as both competing and complementary modes. Results show that the model captures detailed individual heterogeneity in cycling demand for different trip purposes. Impacts of bicycle infrastructure, land use characteristics and individual/household socio-demographics are investigated. Detailed individual level travel time and generalised cost are derived for cyclists of different socio-demographics. The result highlights the importance of choosing a good measure of generalised cost, given that different bicycle route choice models result in different effects of bicycle infrastructure. In future applications, the model can be used to evaluate proposed bicycle investments regarding their impact on link flow, bicycle route choice, modal shift and generation of completely new tours. The model can also be a powerful tool in a cost-benefit analysis of bicycle investments. [ABSTRACT FROM AUTHOR]

This paper intends to find out which parameters have the highest influence on bicyclists' route-choice-behaviour and how they contribute. There are many attributes considered throughout different studies that are favoured by cyclists. The attributes are the basis for generating a function which predicts the route choice of cyclists. The paper aims at making a comparison between distance-based routes and attribute-based routes. The model for creating the bicycle route choice program is based on the network model of Norrköping, Sweden. The preferred attributes of the model each assign a weight in the cost function of the model for each link of the urban network. The algorithm of the lowest cost function route searches the shortest path in terms of assigned link costs over the whole network. For comparing the results of the cost function and the shortest route (between an origin-destination), the model has a shortest path finding algorithm between different Origin and Destination pairs implemented. It has been viewed that around 25% of all cumulative routes do not show any distance differences between the shortest path distance and attributes based solution. With the used weights of the Standard scenario, it can be seen that the trade-off between shortest distance and safety respectively comfort can be optimized, fulfilling both criteria (distance and safety/comfort). [ABSTRACT FROM AUTHOR]