Requirements for traffic assignment models for strategic transport planning: A critical assessment

Transport planning models are used all over the world to assist in the decision making regarding investments in infrastructure and transport services. Traffic assignment is one of the key components of transport models, which relate travel demand to infrastructure supply, by simulating (future) route choices and network conditions, resulting in traffic flows, congestion, travel times, and emissions. Cost benefit analyses rely on outcomes of such models, and since very large monetary investments are at stake, these outcomes should be as accurate and reliable as possible. However, the vast majority of strategic transport models still use traditional static traffic assignment procedures with travel time functions in which traffic flow can exceed capacity, delays are predicted in the wrong locations, and intersections are not properly handled. On the other hand, microscopic dynamic traffic simulation models can simulate traffic very realistically, but are not able to deal with very large networks and may not have the capability of providing robust results for scenario analysis. In this paper we discuss and identify the important characteristics of traffic assignment models for transport planning. We propose a modelling framework in which the traffic assignment model exhibits a good balance between traffic flow realism, robustness, consistency, accountability, and ease of use. Furthermore, case studies on several large networks of Dutch and Australian cities will be presented

Generalized Multivariate Extreme Value Models for Explicit Route Choice Sets

This paper analyses a class of route choice models with closed-form probability expressions, namely, Generalized Multivariate Extreme Value (GMEV) models. A large group of these models emerge from different utility formulas that combine systematic utility and random error terms. Twelve models are captured in a single discrete choice framework. The additive utility formula leads to the known logit family, being multinomial, path-size, paired combinatorial and link-nested. For the multiplicative formulation only the multinomial and path-size weibit models have been identified; this study also identifies the paired combinatorial and link-nested variations, and generalizes the path-size variant. Furthermore, a new traveller's decision rule based on the multiplicative utility formula with a reference route is presented. Here the traveller chooses exclusively based on the differences between routes. This leads to four new GMEV models. We assess the models qualitatively based on a generic structure of route utility with random foreseen travel times, for which we empirically identify that the variance of utility should be different from thus far assumed for multinomial probit and logit-kernel models. The expected travellers' behaviour and model-behaviour under simple network changes are analysed. Furthermore, all models are estimated and validated on an illustrative network example with long distance and short distance origin-destination pairs. The new multiplicative models based on differences outperform the additive models in both tests

Quasi-dynamic network loading: Adding queuing and spillback to static traffic assignment

For many years, static traffic assignment models have been widely applied in transport planning studies and will continue to be an important tool for strategic policy decisions. As is well known, in the traditional approach, the location of the delays and queues are not predicted correctly, and the resulting travel times do not correspond well with reality. Dynamic models can approach reality much better, but come at a computational cost. In this paper we propose a quasi-dynamic model which inherits most of the computational efficiency of static models, but aims to keep most of the important dynamic features, such as queuing, spillback, and shockwaves. Instead of adjusting the traditional static model or using heuristics, we theoretically derive the model from the dynamic link transmission model, assuming stationary travel demand and instantaneous flow. Furthermore, we present algorithms for solving the model. On a corridor network we illustrate the feasibility and compare it with other approaches, and on a larger network of Amsterdam we discuss the computational efficiency

Capacity constrained stochastic static traffic assignment with residual point queues incorporating a proper node model

Static traffic assignment models are still widely applied for strategic transport planning purposes in spite of the fact that such models produce implausible traffic flows that exceed link capacities and predict incorrect congestion locations. There have been numerous attempts in the literature to add capacity constraints to obtain more realistic traffic flows and bottleneck locations, but so far there has not been a satisfactory model formulation. After reviewing the literature, we come to the conclusion that an important piece of the puzzle has been missing so far, namely the inclusion of a proper node model. In this paper we propose a novel path-based static traffic assignment model for finding a stochastic user equilibrium in which we include a first order node model that yields realistic turn capacities, which are then used to determine consistent traffic flows and residual point queues. The route choice part of the model is specified as a variational inequality problem, while the network loading part is formulated as a fixed point problem. Both problems are solved using existing techniques. We illustrate the model using hypothetical examples, and also demonstrate feasibility on large-scale networks

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening

Estimation of the Change in Cumulative Flow over Probe Trajectories using Detector Data

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Estimation of the Change in Cumulative Flow over Probe Trajectories using Detector Data

Detector data can be used to construct cumulative flow curves, which in turn can be used to estimate the traffic state. However, this approach is subject to the cumulative error problem. Multiple studies propose to mitigate the cumulative error problem using probe trajectory data. These studies often assume “no overtaking” and thus that the cumulative flow is zero over probe trajectories. However, in multi-lane traffic this assumption is often violated. Therefore, we present an approach to estimate the change in cumulative flow along probe trajectories between detectors based on disaggregated detector data. The approach is tested with empirical data and in microsimulation. This shows that the approach is a clear improvement over assuming “no overtaking” in free-flow conditions. However, the benefits are not clear in varying traffic conditions. The approach can be applied in practice to mitigate the cumulative error problem and estimate the traffic state based on the resulting cumulative flow curves. As the performance of the approach depends on the changes in traffic conditions, it is suggested to use the probe speed observations between detectors to assign an uncertainty to the change in cumulative flow estimates. Furthermore, a potential option for future work is to use more elaborate schemes to estimate the probe relative flow between detectors, which may, for instance, combine probe speeds with estimates of the macroscopic states along the probe trajectory. If these macroscopic estimates are based on the cumulative flow curves at the detector locations, this would result in an iterative approach.Transport and PlanningTransport and Plannin

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening