Otto Anker Nielsen

A Stochastic Route Choice Model for Car Travellers in the Copenhagen Region

The paper presents a large-scale stochastic road traffic assignment model for the Copenhagen Region. The model considers several classes of passenger cars (different trip purposes), vans and trucks, each with its own utility function on which route choices are based. The utility functions include distributed coefficients (Error Components) estimated on SP-data in a mixed logit model. This was compared with a traditional nested logit model. A number of alternative formulations of EC were tested, and the resulting distributions of value of times are discussed. In application, the different classes and types of vehicles influence all the speed-flow relationships on links within an equilibrium framework. Sub-models for intersections and roundabouts describe queues and geometric delays.

The AKTA Road Pricing Experiment in Copenhagen

This chapter presents the AKTA road pricing experiment in Copenhagen and its main results. Conclusions are drawn on the behavioural impacts of the different pricing schemes and the accuracy of different survey and modelling techniques. AKTA followed 500 car users equipped with a GPS-based device in their cars. The participants’ normal travel pattern was estimated using observations from a control period, after which pricing schemes (toll versus km-based) were implemented over an 8 to 12-week period. The participants earned the money they saved by changing behaviour compared to the control period. Surprisingly, it turned out that the participants’ behavioural changes were greater than expected based on prior surveys and modelling. Habits may have been expected to reduce changes, but the rather high amount of money involved (budget constraints) seems to be more important. Changes were analysed concerning the time of day, number of trips, average length of trips, costs of trips, etc. It appeared that km-based charging systems were more efficient than multi cordon-based.

ASSESSMENT OF TRAFFIC NOISE IMPACTS

A steady growth in traffic intensities in most urban areas throughout the world has forced planners and politicians to seriously consider the resulting environmental impact, such as traffic noise, accidents and air pollution. The assessment of such negative factors is needed in order to reveal the true social benefit of infrastructure plans. The paper presents a noise assessment model for the Copenhagen region, which brings together GIS technology and non‐linear hedonic regression models to reveal the implicit costs of traffic noise measured as the marginal percentage loss in property values with respect to the decibel traffic noise. The model distinguishes between houses and apartments and shows that the ability to include refined accessibility variables have significant impact on estimated prices.

Multimodal route choice models of public transport passengers in the Greater Copenhagen Area

Understanding route choice behavior is crucial to explain travelers’ preferences and to predict traffic flows under different scenarios. A growing body of literature has concentrated on public transport users without, however, concentrating on multimodal public transport networks because of their inherent complexity and challenges. In particular, choice set generation and modeling route choice behavior while accounting for similarity across alternatives and heterogeneity across travelers are non-trivial challenges. This paper tackles these challenges by focusing on the revealed preferences of 5,641 public transport users in the Greater Copenhagen Area. A two-stage approach consisting of choice set generation and route choice model estimation allowed uncovering the preferences of the users of this multimodal large-scale public transport network. The results illustrate the rates of substitution not only of the in-vehicle times for different public transport modes, but also of the other time components (e.g., access, walking, waiting, transfer) composing the door-to-door experience of using a multimodal public transport network, differentiating by trip length and purpose, and accounting for heterogeneity across travelers.

Passenger Perspectives in Railway Timetabling: A Literature Review

ABSTRACT When looking at railway planning, a discrepancy exists between planners who focus on the train operations and publish fixed railway schedules, and passengers who look not only at the schedules but also at the entirety of their trip, from access to waiting to on-board travel and egress. Looking into this discrepancy is essential, as assessing railway performances by merely measuring train punctuality would provide an unfair picture of the level of service experienced by passengers. Firstly, passengers’ delays are often significantly larger than the train delays responsible for the passengers to be late. Secondly, trains’ punctuality is often strictly related to too tight schedules that in turn might translate into knock-on delays for longer dwelling times at stations, trip delays for increased risk of missing transfer connections, and uncertain assessment of the level of service experienced, especially with fluctuating passenger demand. A key aspect is the robustness of railway timetables. Empirical evidence indicates that passengers give more importance to travel time certainty than travel time reductions, as passengers associate an inherent disutility with travel time uncertainty. This disutility may be broadly interpreted as an anxiety cost for the need for having contingency plans in case of disruptions, and may be looked at as the motivator for the need for delay-robust railway timetables. Interestingly, passenger-oriented optimisation studies considering robustness in railway planning typically limit their emphasis on passengers to the consideration of transfer maintenance. Clearly, passengers’ travel behaviour is far more complex and multi-faceted and thus several other aspects should be considered, as becoming more and more evident from passenger surveys. The current literature review starts by looking at the parameters that railway optimisation/planning studies are focused on and the key performance indicators that impact railway planning. The attention then turns to the parameters influencing passengers’ perceptions and travel experiences. Finally, the review proposes guidelines on how to reduce the gap between the operators’ railway planning and performance measurement on the one hand and the passengers’ perception of the railway performance on the other hand. Thereby, the conclusions create a foundation for a more passenger-oriented railway timetabling ensuring that passengers are provided with the best service possible with the resources available.

A Large Scale Stochastic Multi-Class Schedule-Based Transit Model with Random Coefficients

Public transport assignment models are increasing in complexity in order to describe passengers’ route choices as detailed and correctly as possible. Important trends in the development are 1) schedule-based models, 2) inclusion of feeder modes, 3) use of stochastic components to describe differences in passengers’ preferences within and between trip purposes and classes, as well as to describe non-explained variation within a utility theory framework, and 4) consideration of capacity problems at coach level, system level and terminal level. In the East Denmark Model, such a large-scale transit assignment model was developed and estimated within a Stochastic User Equilibrium framework solved by the Method of Successive Averages. The model covered a metropolitan area including its hinterland (2,000 lines, 50,000 runs, 300,000 stops, and 1 million nodes and 10 million arcs in the calculation graph). The paper outlines the experiences from this project, and the subsequent research and development using the case as ‘modelling laboratory’. The main focus of the paper is to describe the estimation of the utility functions, and to discuss and suggest optimisation of the solution algorithm.

Simulation of Disturbances and Modelling of Expected Train Passenger Delays

This paper describes how forecasting of regularity for railway systems have traditionally – if at all – been computed for trains, not for passengers. It has only relatively recently become possible to model and evaluate the actual passenger delays. This paper describes how it is possible to use a passenger regularity model to estimate the actual passenger delays. The combination of the passenger regularity model with railway simulation software is described, demonstrating the possibility of predicting future passenger delays. The described passenger regularity model is run daily to calculate the passenger delays of the Copenhagen suburban rail network the previous day. The results obtained with the passenger regularity model used together with the simulation software are very similar to the daily calculated passenger regularity of the Copenhagen suburban network. As the combined method includes simulation software and reflects the actual passenger regularity, it is possible to use a combination of a passenger regularity model and simulation software to evaluate and compare future scenarios.

Estimating passenger numbers in trains using existing weighing capabilities

Knowing passenger numbers is important for the planning and operation of the urban rail systems. Manual and electronic counting systems (typically infrared or video) are expensive and therefore entail small sample sizes. They usually count boarding and alighting passengers, which means that errors in estimates of total numbers of passengers propagate along train runs. Counting errors in manual and electronic counting systems are typically flow-dependent, making uncertainty a function of volume. This paper presents a new counting technique that exploits the weighing systems installed in most modern trains to control braking. This technique makes passenger counting cheaper and ensures a complete sample. The paper compares numbers estimated by this technique with manual counts and counts from an infrared system in trains in urban Copenhagen. It shows that the weighing system provides more accurate passenger counts than the infrared equipment. The method has been validated on a large data set and is now in full operation in the urban Copenhagen rail system.

Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area

Abstract GPS data collection has become an important means of investigating travel behaviour. This is because such data ideally provide far more detailed information on route choice and travel patterns over a longer time period than possible from traditional travel survey methods. Wearing a GPS unit is furthermore less requiring for the respondents than filling out (large) questionnaires. It places however high requirements to the post-processing of the data. This study developed and tested a combined fuzzy logic and GIS-based algorithm to process raw GPS data. The algorithm is applied to GPS data collected in the highly complex large-scale multi-modal transport network of the Greater Copenhagen area. It detects trips, trip legs and distinguishes between five modes of transport. The algorithm was validated by comparing with a control questionnaire collected among the same persons and a sensitivity analysis was performed. This showed that the algorithm (i) identified corresponding trip legs for 82% of the reported trip legs, (ii) avoided classifying non-trips such as scatter around activities as trip legs, (iii) identified the correct mode of transport for more than 90% of trip legs, and (iv) were robust towards the specification of the model parameters and thresholds. The method thus makes it possible to use GPS for travel surveys in large-scale multi-modal networks.

Estimating Value of Congestion and of Reliability from Observation of Route Choice Behavior of Car Drivers

In recent years, a consensus has been reached about the relevance of calculating the value of congestion and the value of reliability for better understanding and therefore better prediction of travel behavior. The current study proposed a revealed preference approach that used a large amount of GPS data from probe vehicles to provide insight into actual behavior in choosing a route. Mixed path size correction logit models were estimated from samples of 5,759 observations in the peak period and 7,964 observations in the off-peak period, while a mean-variance model was specified to consider both congestion and reliability terms. Results illustrated that the value of time and the value of congestion were significantly higher in the peak period because of possible higher penalties for drivers being late and consequently possible higher time pressure. Moreover, results showed that the marginal rate of substitution between travel time reliability and total travel time did not vary across periods and traffic conditions, with the obvious caveat that the absolute values were significantly higher for the peak period. Last, results showed the immense potential of exploiting the growing availability of large amounts of data from cheap and enhanced technology to obtain estimates of the monetary value of different travel time components from the observation of actual behavior, with arguably potential significant impact on the realism of large-scale models.