Learning from a Failed Innovation Process: Personal Rapid Transit for a Dutch City

The technological and institutional innovation process in public transport is slow and difficult to control. Because many forces work on this process, predicting its outcomes is difficult. This was demonstrated in a project in Eindhoven, the Netherlands, where a pilot was prepared to demonstrate the potential of personal rapid transit. The pilot was part of an innovation process to lead to a more sustainable urban transport system. The guidelines of the bureau for sustainable technological development were followed to guarantee the long-term feasibility and effectiveness of the process. Although the process was designed carefully with concern for the predictable risks, the pilot had to be stopped because of unforeseen political problems. A change of policy priorities and key people caused the project’s failure. In addition, strict regulations for tendering bids slowed the process and favored bidders without project knowledge. This paper analyzes the different risk factors and gives conclusions for improving chances for success in future similar innovation projects. Checklists provide a tool to make an ex ante analysis of an innovation project’s feasibility and to give guidance for optimal conditions for success

Probabilistic models for queues at fixed control signals

The estimation of queues at signalized intersections is a classical problem in transportation engineering and operations research. Nevertheless, a general theory able to explain how queues form and cause delays to the drivers is still missing. Typically, queue dynamics are modelled as deterministic, causal phenomena, and under rather limiting assumptions; however, especially in urban networks, these are far from being deterministic or certain. This paper presents a new probabilistic queuing model that can explain the dynamic and stochastic behaviour of queues at fixed controlled signals. The probabilistic approach allows one to capture the temporal behaviour of queues, and to measure the uncertainty of a queue state prediction by computing the evolution of its probability in time, assumed any temporal distribution of the arrivals. This can be fundamental information in, e.g., travel time estimation, network reliability, design and planning of urban areas, and to estimate complex effects that can be observed in congested networks such as spillback or gridlock. Comparison with microscopic simulation shows very good consistency both under the assumption of stationary and non-stationary arrival distributions.Queuing theory Probability theory and Markov Chains Fixed controls Spillback

Consistent link flow estimation from counts

Partly because of counting errors and partly because counts may be carried out on different days, traffic counts on links of a network are unlikely to satisfy the flow conservation constraint "flow IN = flow out" at every node of the network. Van Zuylen and Willumsen (1980) have described a method of eliminating inconsistencies in traffic counts when a single count is available for each link in the network. In this paper, the method is extended to the case when more than one count is available on some links of the network. In addition, an algorithm is described for application of the method.

Travel time unreliability on freeways: Why measures based on variance tell only half the story

In recent years, travel time reliability has become one of the key performance indicators of transportation networks and corridors around the globe. Travel time reliability indicators are mostly related to properties of the day-to-day travel time distribution on for example a freeway corridor. On the basis of empirical data a number of key characteristics of this day-to-day distribution can be identified. Most importantly, this distribution is not only very wide but also heavily skewed. The (economic) consequences of this skew are substantial. For example, it is shown that in some peak periods the 5% most "unlucky drivers" incur almost five times as much delay as the 50% most fortunate travelers. We argue this implies first of all that (besides the variance of travel times) skew must be considered an important contributing factor to travel time unreliability. Secondly, it suggests that most of currently used unreliability measures (which are predominantly based on travel time variance), should be used and interpreted with some reservations, since they only account for a part of the costs (that is, delays) of unreliability. This is further substantiated by a comparison on the basis of empirical data from a densely used freeway in the Netherlands between a new travel time reliability measure based on both width and skew, and a number of travel time reliability measures commonly used in practice. The analysis clearly illustrates the inconsistency between all measures, both old and new. In illustration, in cases where the commonly used misery index dubs a particular departure period very unreliable, another common measure (buffer time) considers these periods relatively reliable. Although without objective and quantitative criteria (e.g. economic or societal costs) a choice for any of these measures in road network performance analyses will remain subject to debate, this article provides empirically underpinned arguments to prefer measures incorporating the skew of the travel time distribution.

Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters

A hybrid model for predicting urban arterial travel time on the basis of so-called state-space neural networks (SSNNs) and the extended Kalman filter (EKF) is presented. Previous research demonstrated that SSNNs can address complex nonlinear spatiotemporal problems. However, SSNN models require off-line training with large sets of input–output data, presenting three main drawbacks: (a) great amounts of time and effort are involved in collecting, preparing, and executing these training sessions; (b) as the input–output mapping changes over time, the model requires complete retraining; and (c) if a different input set becomes available (e.g., from inductive loops) and the input–output mapping has to be changed, then retraining the model is impossible until enough time has passed to compose a representative training data set. To improve SSNN effectiveness, the EKF is proposed to train the SSNN instead of conventional approaches. Moreover, this network topology is derived from the urban travel time prediction problem. Instead of treating the neural network as a “black-box” model, the design explicitly reflects the relationships that exist in physical traffic systems. It allows the interpretation of neuron weights and structure in terms of the inherent mechanism of the network process with clear physical meaning. Model performance was tested on a densely used urban arterial in the Netherlands. Performance of this proposed model is compared with that of two existing models. Results of the comparisons indicate that the proposed model predicts complex nonlinear urban arterial travel times with satisfying effectiveness, robustness, and reliability

Advanced OR and AI Methods in Transportation AN AGENT BASED DISTRIBUTED MIRCOSCOPIC ONLINE SIMULATION MODEL

Abstract. To cope with the demand for mobility, which will further increase in the future, the infrastructure has to be used more efficiently. Therefore network operation control is needed to interfere with the traffic. The basis of operational control should be an accurate online estimation of the actual traffic situation and a prediction of the future. The microscopic online simulator MiOS will be presented which allows distributed online simulation for real-time operations. Results are shown for a medium size network of the City of Delft. 1

Advanced OR and AI Methods in Transportation A SELF-LEARNING-PROCESS BASED DECISION SUPPORT SYSTEM FOR BEIJING TRAFFIC MANAGEMENT

developed for Beijing city in China. The inspiration is to be able to propose a best suitable measures for a given (either recurrent or non-recurrent) traffic situation, and to apply it to a real-life traffic management. A major concern is to be able to quickly recognise problems and recommend/retrieve corresponding solutions. To achieve this, 3 major steps are being followed: (1) a matching rule enables to propose a robust solution, against a problem; (2) further search continues to identify a most likely scenario that has been successfully executed before; and (3) most successful scenarios can be prepared offline and stored to a relational database after being tested. This paper proposes a novel self-learning approach using conjointly expert knowledge-based choice and case-based reasoning. Key aspects to support such process include: (a) problem identification that is based on a mesoscopic large-scale network dynamic simulation; and (b) measure evaluation that can be performed according to performance indictors. Effective scenarios (measure to problem) are stored into KBEST (knowledge-based expert system) and made available for offline and online calls. An implementation of such system to incident management is foreseen and being designed. 1