Efficient traffic management requires an understanding of mobility patterns in the road network, where one important component is route choice. This study aims to analyze how route choice models can be adapted to efficient urban traffic management and intelligent transport systems (ITS), by constructing route sets and attributes from GPS and network data. With a route choice model that is responsive to traveltime changes in the network, travel behavior during incidents can be predicted to evaluate traffic management policies, such as traveler information and traffic control. The dataset consists of about 400,000 vehicle trips and is divided into a training dataset and a test dataset. The two datasets are compared, and the experiments show that the routes used are similar. Discrete route choice models are estimated with one data-driven path identification approach (DDPI) and one where the data-driven path set is augmented with routes from a network-based shortest path generation with link penalty (NBPA). The result suggests that the traveltime has a larger impact on the route choice when the model is trained on the NBPA route set and that the route's simplicity, length, and traveltime are important attributes for the route choice, which are useful insights in a traffic management context.

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations.

Mixed traffic with automated and human-driven vehicles interacting with one another will soon become a common reality. Microscopic traffic simulation can preemptively help assess the impact on the traffic flow dynamics as long as the tools adequately capture the differences on how automated driving systems (ADSs) drive compared to humans. In this work a modeling approach that captures differences in perception performance is proposed. While human drivers perceive through their senses and cognitive processes, ADS perceive the driving context through on-board sensors, connectivity features and software. The perception performance is described in terms of accuracy, precision, detection range, and detection delay. The model for perception is implemented in SUMO and a simulation test in a platoon shows the acceleration response affected by up to 35% for perception errors of approximate to 10% which by extension will affect the traffic flow dynamics. The proposed modeling approach for perception contributes to the robustness of microscopic traffic simulation and the modeling of heterogeneous mixed traffic.

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.

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Cost-benefit analysis (CBA) for cycling infrastructure investments are less sophistically developed compared to the ones for private cars and public transport, and one of main reasons is the lack of "well-developed" transport models for cycling. In this study, a dedicated transport model for cycling is used to appraise cycling infrastructure investments in Stockholm, Sweden. The model captures the impact of a change in cycling infrastructure on cycling route choice, mode choice, destination choice and trip generation and calculates cycling flow on link level. the generalised cost measure defined in the route choice model captures the impact of cycling infrastructure. Results suggest that although cycling flow on the links with investment may increase substantially, only a small share comes from modal shift and thus the external effects such as reducing car congestion and emissions are marginal. For all three scenarios investigated, over 97% of the benefits measured in the unit of generalised cost belong to the existing cyclists. The route choice model does not minimize travel time but generalised cost which also measures health, safety benefits and other possible benefits that may be considered by the cyclists when they choose to cycle. In fact, travel time saving benefits of the investments evaluated in this paper are all negative. The existing effect evaluation models therefore need to be adjusted to be more consistent with the transport model.

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level

The introduction of automated vehicles (AVs) is commonly expected to improve different aspects of transportation. A long transition period is expected until AVs become prevalent on roads. During this period, different types of AVs with different driving logics will coexist along human driven vehicles. Using microscopic traffic simulation, this study investigates the range of potential impacts on traffic performance in terms of throughput and travel delays for different types of AVs and human driven vehicles on motorways. The simulation experiment includes scenarios with combinations of three different driving logics for AVs together with human driven vehicles at increasing penetration rates. The utilized AV driving logics represent the evolution of AVs, they were defined in the microscopic simulation tool Vissim and were created by modifying and extending the human driver behaviour models. The results of the simulation experiment show a decrease in vehicle throughput and significant effects on delay times when AVs with a more cautious driving logic are predominant. Overall, results show higher vehicle throughput and lower travel delays as AVs evolve to more advanced driving logics.

Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing classification methods for GPS-trajectories are often inefficient for cellular network data, which has lower resolution in space and time than GPS data.

In this study, we compare three geometry-based mode classification methods and three supervised methods to classify trips extracted from cellular network data in intercity origin-destination pairs as either road or train. To understand the difficulty of the problem, we use a labeled dataset of 255 trips in two OD-pairs to train the supervised classification methods and to evaluate the classification performance. For an OD-pair where the road and train routes are not separated by more than four kilometers, the geometry-based methods classify 4.5% - 7.1% of the trips wrong, while two of the supervised methods can classify all trips correctly. Using a large-scale dataset of 29037 trips, we find that separation between classes is less evident than in the labeled dataset and show that the choice of classification methods impacts the aggregated modal split estimate.

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 () using the original zoning used in the travel demand model with 189 zones, while it is significant with 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 that the choice of the trip extraction method is crucial for the travel demandestimation as we find systematic differences in the resulting travel demand matrices using two different methods.

The aim of the work presented here is to develop a mathematical model that can assist decision-makers in selecting the optimal network of subsidised routes in air transportation. Sweden is used as a case study. The results show that even if most of the Swedish population already has a good accessibility, the model can suggest new routes that can further improve accessibility without increasing the subsidisation cost. The closure of an airport reduces the available commercial routes, which would impair the accessibility to a given destination; a subsidised route would hence be required as a replacement. The ability of the model to consider several accessibility criteria makes it useful for managers at transportation authorities when making airport location decisions.

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 x 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.

The signaling data in cellular networks provide means for analyzing the use of transportation systems. We propose methods that aim to reconstruct the used route through a transportation network from call detail records (CDRs) which are spatially and temporally sparse. The route estimation methods are compared based on the individual routes estimated. We also investigate the effect of different route estimation methods when employed in a complete network assignment for a larger city. Using an available CDR dataset for Dakar, Senegal, we show that the choice of the route estimation method can have a significant impact on resulting link flows.

Heterogeneous data collected by smartphone sensors offer new opportunities to study a person’s mobility behavior. The mobility patterns extracted from the travel histories found in these data enable agents residing in mobile devices to model transitions between visited locations, so that upcoming trips can be predicted after observing a set of events, and assistance can be planned in advance. When several agents cooperate, the forecasted trips made by multiple users can provide a potential supply for shared mobility systems such as dynamic ridesharing. These trips must be sufficiently regular and frequent to be reliably announced as shareable trips. This paper describes a methodology to identify a predictable aggregate supply for ridesharing via mobility patterns discovered in users’ travel histories. The methodology empirically quantifies measures like the regularity and frequency of these patterns on a dataset consisting of 967 users scattered across different geographical areas. The sample exhibits high heterogeneity with respect to these measures (hence, of predictability, regardless of the prediction method). This paper shows how frequency of trip patterns decreases, while regularity increases, when additional dimensions such as departure times are added to the analysis. It was concluded that the traveler flexibility with regard to accepting less regular trips is vital to discover a larger supply. These results provide insights to develop future applications able to take advantage of this approach, to increase ridesharing rates, allowing a critical mass to be more easily attained.

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

To get a better understanding of people’s mobility, cellular network signalling data including location information, is a promising large-scale data source. In order to estimate travel demand and infrastructure usage from the data, it is necessary to identify the trips users make. We present two trip extraction methods and compare their performance using a small dataset collected in Sweden. The trips extracted are compared with GPS tracks collected on the same mobiles. Despite the much lower location sampling rate in the cellular network signalling data, we are able to detect most of the trips found from GPS data. This is promising, given the relative simplicity of the algorithms. However, further investigation is necessary using a larger dataset and more types of algorithms. By applying the same methods to a second dataset for Senegal with much lower sampling rate than the Sweden dataset, we show that the choice of the trip extraction method tends to be even more important when the sampling rate is low. 

Traffic management and traffic information are essential in urban areas, and require a good knowledge about both the current and the future traffic state. Both parametric and non-parametric traffic state prediction techniques have previously been developed, with different advantages and shortcomings. While non-parametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during non-recurring traffic conditions such as incidents and events. Hybrid approaches, combining the two prediction paradigms have previously been proposed by using non-parametric methods for predicting boundary conditions used in a parametric method. In this paper we instead combine parametric and non-parametric traffic state prediction techniques through assimilation in an Ensemble Kalman filter. As non-parametric prediction method a neural network method is adopted, and the parametric prediction is carried out using a cell transmission model with velocity as state. The results show that our hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 minutes into the future, using a prediction horizon of up to 50 minutes 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

Denna rapport presenterar metod och resultat för framtagning av nya restidsfunktioner till trafikprognosverktyget Sampers. Restidsfunktioner innehåller en del som beskriver fördröjning på vägen och en del som beskriver fördröjningen i samband med korsningar. Då det är svårt och dyrt att göra samtidiga mätningar av trafikflöden och restider har en alternativ ansats använts där restidsfunktionerna kalibrerats baserat på beräkningar av korsningsfördröjning för olika korsningsutformningar med korsningsmodellen Capcal. De framtagna restidsfunktionerna har testats och validerats och är nu implementerade i Samperssystemet.

Cellular networks signaling data provide means for analyzing the efficiency of an underlying transportation system and assisting the formulation of models to predict its future use. This paper describes how signaling data can be processed and used in order to act as means for generating input for traditional transportation analysis models. Specifically, we propose a tailored set of mobility metrics and a computational pipeline including trip extraction, travel demand estimation as well as route and link travel flow estimation based on Call Detail Records (CDR) from mobile phones. The results are based on the analysis of data from the Data for development "D4D" challenge and include data from Cote dlvoire and Senegal. (C) 2016 Elsevier B.V. All rights reserved.

Transport planners face a major challenge to devise policies to meet multiple expectations and objectives. While we know that transport networks are complex, multi-modal, and spatially distributed systems, there is now a long history of mathematical tools which assist planners in understanding travel movements. However, the objectives that they are asked to achieve do not always admit such a quantification, and so there is a potential mismatch between seemingly qualitatively driven objectives and quantitatively expressed models of the transport system. In the present chapter we address this mismatch, by focusing on three objectives that we believe represent the typical interests of a planner. These are namely: is the policy economically justifiable (efficient), is it “fair” (equitable), and is it justifiable to a democratic society (acceptable)? We provide mathematical representations of these three objectives and link them to mathematical theory of transport networks, in which we may explore the sensitivity of travel behaviour (and hence the objectives) to various multi-modal transport policies. The detailed steps for representing the policy objectives and sensitivities in the network are set out, and the results of a case study reported in which road tolls, road capacities, and bus fares are the policy variables. Overall, the chapter sets out a systematic method for planners to choose between multi-modal policies based on these three objectives.

The use of mobile phone data for planning of transport infrastructure has been shown to have great potential in providing a means of analyzing the efficiency of a transportation system and assisting in the formulation of transport models to predict its future use. In this paper we describe how this type of data can be processed and used in order to act as both enablers for traditional transportation analysis models, and provide new ways of estimating travel behavior. Specifically, we propose a technique for describing the travel demand by constructing time sliced origin destination matrices which respect the level of detail available in Call Detail Records (CDR) from mobile phone use.

When analyzing large quantities of human mobility traces, the aspects of sensitivity of traces to be analyzed, and the scale at which such analysis can be accounted for is of high importance. The sensitivity implies that identifiable information must not be inferred from the data or any analysis of it. Thus, prompting the importance of maintaining privacy during or post-analysis stages. We aggregate the raw data with the goal to retain relevant information while at the same time discard sensitive user specifics, through site sequence clustering and frequent sequence extraction. These techniques have at least three benefits: data reduction, information mining, and anonymization. Further, the paper reviews the aggregation techniques with regard to privacy in a post-processing step.

The approaches presented in the paper for estimation of travel demand and route choices, and the additional privacy analysis, build a comprehensive framework usable in the processing of mobile phone data for transportation planning.

The project presented in this paper a part of the D4D-Senegal challenge.

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.

As congestion pricing has moved from theoretical ideas in the literature to real world implementations, the need for decision support when designing the 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 optimization 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 optimizing the toll levels in the current cordon, and optimizing both the toll locations and their corresponding toll levels are evaluated. It is shownthat by tuning the toll levels in the current congestion pricing cordon used in Stockholm, the welfare gain can be significantly increased, and furthermore improved by allowing a toll on the bypass highway “Essingeleden”. It is also shown that by optimizing both the 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 forth of the tollable links.

In this paper we evaluate the output from four headway-based public transport model variants for modeling the public transports in Stockholm, Sweden. The results from the four models are analyzed and compared to trip observations. The comparisons are based on model instances where the parameters in the generalized travel time function are calibrated. The best possible parameter values have been found using the calibration procedures SPSA and Compass search. Two different objective functions have been evaluated for the calibration.

Numerical experiments have been performed using a public transport model implemented in Visum by Storstockholms lokaltrafik. For the calibration and analysis, trip observations from the Swedish national travel survey and data generated from a public transport trip planner are used.

From the numerical results, it is concluded that it is of less importance to find the best possible parameter values in the generalized cost function than selecting the best model variant. For the Stockholm public transport model, the numerical results indicate that the models in the class of Random departure time models result in a better fit to the observed trips than the models in the class of Optimal strategies.

Signaling data from the cellular networks can provide a means of analyzing the efficiency of a deployed transportation system and assisting in the formulation of transport models to predict its future use. An approach based on this type of data can be especially appealing for transportation systems that need massive expansions, since it has the added benefit that no specialized equipment or installations are required, hence it can be very cost efficient.

Within this context in this paper we describe how such obtained data can be processed and used in order to act as enablers for traditional transportation analysis models. We outline a layered, modular architectural framework that encompasses the entire process and present results from initial analysis of mobile phone call data in the context of mobility, transport and transport infrastructure. We finally introduce the Mobility Analytics Platform, developed by Ericsson Research, tailored for mobility analysis, and discuss techniques for analyzing transport supply and demand, and give indication on how cell phone use data can be used directly to analyze the status and use of the current transport infrastructure.

Differentiated tolls for cars and heavy goods vehicles (HGVs) may improve the efficiency of congestion pricing schemes. Not only do private trips by car and commercial activities by HGV differ in how the time is valued by the user/operator, in general, one additional truck will also contribute to the congestion more than one additional car. In this paper we show how different modeling approaches for HGV route choice and demand may affect the evaluation of road pricing schemes. Also, we provide a small example to illustrate potential problems, and present results for a larger model of the German city Stuttgart.

Real-time estimations of current and future traffic states are an essential part of traffic management and traffic information systems. Within the Mobile Millennium project considerable effort has been invested in the research and development of a real-time estimation system that can fuse several sources of data collected in California. During the past year this system has been adapted to also handle traffic data collected in Stockholm. This paper provides an overview of the model used for highways and presents results from an initial evaluation of the system. As part of the evaluation process, GPS data collected in an earlier field-test and estimations generated by the existing system used by the TMC in Stockholm, are compared with the estimations generated by the Mobile Millennium system. Given that the Mobile Millennium Stockholm system has not undergone any calibration, the results from the evaluation are considered promising. The estimated travel times correspond well to those measured in the field test. Furthermore, the estimations generated by the Mobile Millennium system can be regarded as superior to those of existing traffic management system in Stockholm. The highway model was found to perform well even with a reduction in the number of sensors providing data. The findings of this study indicate the robustness of the Mobile Millennium system and demonstrate how the system can be migrated to other geographical areas with similar sources of available data.

The feasible direction method of Frank and Wolfe has been claimed to be efficient for solving the stochastic transportation problem. While this is true for very moderate accuracy requirements, substantially more efficient algorithms are otherwise diagonalized Newton and conjugate Frank–Wolfe algorithms, which we describe and evaluate. Like the Frank–Wolfe algorithm, these two algorithms take advantage of the structure of the stochastic transportation problem. We also introduce a Frank–Wolfe type algorithm with multi-dimensional search; this search procedure exploits the Cartesian product structure of the problem. Numerical results for two classic test problem sets are given. The three new methods that are considered are shown to be superior to the Frank–Wolfe method, and also to an earlier suggested heuristic acceleration of the Frank–Wolfe method.

Designing a congestion pricing scheme involves a number of complex decisions.Focusing on the quantitative parts of a congestion pricing system with link tolls, the problem involves findingthe number of toll links, the link toll locations and their corresponding toll level and schedule.In this paper, we develop and evaluate methods for finding the most efficient design for a congestion pricing scheme in a road network model with elastic demand. The design efficiency is measured by the net social surplus, which is computed as the difference between the social surplus and the collection costs (i.e. setup and operational costs) of the congestion pricing system. The problem of finding such a scheme is stated as a combinatorial bi-level optimization problem. At the upper level, we maximize the net social surplus and at the lower level we solve a user equilibrium problem with elastic demand, given the toll locations and toll levels,to simulate the user response. We modify a known heuristic procedure for finding the optimal locations and toll levels given a fixed number of tolls to locate, to find the optimal number of toll facilities as well. A new heuristic procedure, based on repeated solutions of a continuous approximation of the combinatorial problem is also presented. Numerical results for two small test networks are presented. Both methods perform satisfactorily on the two networks. Comparing the two methods, we find that the continuous approximation procedure is the one which shows the best results.

This paper addresses the problem of designing a road congestion pricing scheme withlink tolls. The problem involves decisions on where to locate the toll collecting facilitiesand what tolls to charge the road users. We formulate this problem as a bi-levelprogram, with the objective to maximize the net social surplus, which include the costof setting up and operate the toll collection system. A previously developed heuristicmethod is applied to find close to optimal toll locations and charges for a traffic networkrepresenting the Stockholm region. The result is compared with the current congestionpricing scheme in Stockholm.

 In this paper we extend a previously developed heuristic procedure, with a modal choice model, to solve the congestion pricing problem of simultaneously finding the optimal number of toll facilities, their corresponding location and toll levels. When considering a congestion pricing scheme the cost of collecting the tolls can not be disregarded. The objective is where-fore to maximize the net social surplus, which is the social surplus minus the cost of collect-ing the tolls. The heuristic method is an iterative solution procedure, in which the integer part of the objec-tive function is approximated by a continuous function. A version of the Sioux Falls network (76 links) is used to demonstrate the solution procedure. The solution is a congestion pricing scheme which divide the network into four zones, by locating tolls on 27 links. This solution yields a social surplus which is only 13.5% lower than the marginal social cost pricing solu-tion. 

Designing a congestion pricing scheme involves a number of complex decisions. Focusing on the quantitative parts of a congestion pricing system with link tolls, the problem involves finding the number of tolled links, the link toll locations and their corresponding toll level and schedule. In this paper, we develop and evaluate methods for finding a most efficient design of a congestion pricing scheme in a road network with elastic demand. The design efficiency is measured by the net social surplus, which is computed as the difference between the social surplus and the collection costs (i.e. setup and operation cost) of the congestion pricing system. The problem of finding such a scheme is stated as a combinatorial bi-level optimization problem. On the upper level we maximize the net social surplus and on the lower level we solve a user equilibrium problem with elastic demand, given the toll locations and toll levels, to simulate the user response. We modify a known heuristic procedure for finding the optimal locations and toll levels given a fixed number of tolls to locate, to find the optimal number of tolls to locate as well. A new heuristic procedure is also presented which is based on repeated solutions of a continuous approximation of the combinatorial problem. Numerical results for two small scale test networks are presented. Both methods perform satisfactory on the two networks. Comparing the two methods, the iterative approximation procedure is the one which shows the best results. The results are compared to solutions obtained by an exhaustive search.

This paper describes a method for estimating parameters in volume delay functions. The volume delay function is a central part of static traffic assignment models and describes how the travel time on road link changes with traffic demand. The proposed estimation method is based on that the volume delay function is divided into two parts, one part describing the link travel time and delay and one part describing the intersection delay. The parameters for the link and intersection parts are estimated separately. Collecting data for the link part is seldom a problem. However, earlier experiences have shown that it is both difficult and expensive to collect data on intersection delay. We have used an approach in which the intersection delay data is taken from a model for calculating intersection delay, in this case the analytical intersection model CAPCAL. The developed estimation method has been exemplified on one road type and road environment. A sensitivity analysis has been conducted in order to investigate how large influence that the different assumptions on the road factors have on the volume delay function. The conclusions are that the flow levels on the cross road, the intersection density, the share of straight forward traffic, and the share of different intersection types has the largest influence, and thus should be prioritized when collecting road type data. 

We present a sequential linear programming, SLP, algorithm in which the traditional line-search step is replaced by a multi-dimensional search. The algorithm is based on inner approximations of both the primal and dual spaces, which yields a method which in the primal space combines column and constraint generation. The algorithm does not use a merit function, and the linear programming subproblem of the algorithm differs from the one obtained in traditional methods of this type, in the respect that linearized constraints are taken into account only implicitly in a Lagrangiandual fashion. Convergence to a point that satisfies the Karush-Kuhn-Tucker conditions is established. We apply the new method to a selection of the Hoch-Schittkowski’s nonlinear test problems and report a preliminary computational study in a Matlab environment. Since the proposed algorithmcombines column and constraint generation, it should be advantageous with large numbers of variables and constraints.

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.

We present a solution algorithm for an inverse nonlinear multicommodity network flow problem. This problem is to find link cost adjustments that make a given target link flow solution optimal in a nonlinear multicommodity network flow problem, and that are optimal with respect to a specified objective. The solution procedure uses column generation. We present computational results for instances where the nonlinear multicommodity network flow problems are small and medium scale traffic equilibrium problems, and where system optimal link flows are targeted. The computational results show that the solution procedure is a viable approach for solving medium-scale instances of the inverse traffic equilibrium problem.

Strategic traffic management aims at the improvement of the functionality of the traffic network. This functionality is typically expressed in terms of the traffic flows and the travel times in the network. In this thesis a methodology for decision support in long-term traffic management is proposed. The core of the methodology is a traffic flow model in which it is assumed that the travelers choose their routes in accordance with the wardrop user equilibrium principle. The management goals, regarding traffic flows and travel times in the network, are presumed to be described by constraints. It is further presumed that a set of admissible actions in the network, to be used for achieving the goals, is specified. The proposed approach constitutes a systematic methodology for finding appropriate changes in the traffic network in order to fulfill the management goals.

The methodology includes two stages, and each stage amounts to solving a convex optimization problem. The problem in the first stage is a side constrained traffic equilibrium problem, for which a column generation procedure is proposed. This procedure is tested numerically on well known traffic equilibrium problem instances where link flow capacity constraints and general linear side constraints are added. The computational results are promising, especially for instances with a relatively small number of side constraints, which is likely to be the case in real-life applications.

The problem in the second stage is an inverse nonlinear multicommodity network flow problem, that is, the problem of finding a minimal cost link pricing scheme in a network, such that a given target link flow solution is optimal in a nonlinear multicommodity network flow problem. We present a solution algorithm for the inverse nonlinear multicommodity network flow problem. The algorithm is based on column generation. We present computational results for instances where the nonlinear multicommodity network flow problems are small and medium scale traffic equilibrium problems.

In extensive numerical illustrations it is shown how the decision support methodology can be applied to some typical traffic management scenarios. The ordering of the computations and the flexibility and the shortcomings of the procedure are exemplified.