Change of Scale and Forecasting with the Control-Function Method in Logit Models

Endogeneity is a model misspecification that precludes the consistent estimation of the model parameters. The control-function method is the most suitable tool to address endogeneity for several discrete choice models that are relevant in transportation research. However, the estimators obtained with the control-function method are consistent only up to a scale. In this paper, we first depict the determinants of this change of scale by adapting an existing result for omitted orthogonal attributes in logit models. Then, we study the problem of forecasting under these circumstances. We show that a procedure proposed in previous literature may lead to significant biases, and we suggest novel alternatives to be used with synthetic populations. We use Monte Carlo experimentation and real data on residential location choice to illustrate these results. The paper finishes by summarizing the findings of this investigation and suggesting future lines of research in this area.MIT-Portugal Progra

Spatial accessibility and social inclusion: The impact of Portugal's last health reform

Health policies seek to promote access to health care and should provide appropriate geographical accessibility to each demographical functional group. The dispersal demand of health‐careservices and the provision for such services atfixed locations contribute to the growth of inequality intheir access. Therefore, the optimal distribution of health facilities over the space/area can lead toaccessibility improvements and to the mitigation of the social exclusion of the groups considered mostvulnerable. Requiring for such, the use of planning practices joined with accessibility measures. However,the capacities of Geographic Information Systems in determining and evaluating spatial accessibility inhealth system planning have not yet been fully exploited. This paper focuses on health‐care services planningbased on accessibility measures grounded on the network analysis. The case study hinges on mainlandPortugal. Different scenarios were developed to measure and compare impact on the population'saccessibility. It distinguishes itself from other studies of accessibility measures by integrating network data ina spatial accessibility measure: the enhanced two‐stepfloating catchment area. The convenient location forhealth‐care facilities can increase the accessibility standards of the population and consequently reducethe economic and social costs incurred. Recently, the Portuguese government implemented a reform thataimed to improve, namely, the access and equity in meeting with the most urgent patients. It envisaged,in terms of equity, the allocation of 89 emergency network points that ensured more than 90% of thepopulation be within 30 min from any one point in the network. Consequently, several emergency serviceswere closed, namely, in rural areas. This reform highlighted the need to improve the quality of the emergencycare, accessibility to each care facility, and equity in their access. Hence, accessibility measures becomean efficient decision‐making tool, despite its absence in effective practice planning. According to anapplication of this type of measure, it was possible to verify which levels of accessibility were decreased,including the most disadvantaged people, with a larger time of dislocation of 12 min between 2001 and 2011

Modifications of comet materials by the sublimation process: Results from simulation experiments

An active comet like comet Halley loses by sublimation a surface layer of the order of 1 m thickness per perihelion passage. In situ measurements show that water ice is the main constituent which contributes to the gas emission although even more volatile species (CO, NH3, CH4, CO2 etc.) have been identified. Dust particles which were embedded in the ices are carried by the sublimating gases. Measurements of the chemical composition of cometary grains indicate that they are composed of silicates of approximate chondritic composition and refractory carbonaceous material. Comet simulation experiments show that significant modifications of cometary materials occur due to sublimation process in near surface layers which have to be taken into account in order to derive the original state of the material

Reducing the Dimension of Online Calibration in Dynamic Traffic Assignment Systems

Effective real-time traffic management strategies often require dynamic traffic assignment systems that are calibrated online. But the computationally intensive nature of online calibration limits their application to smaller networks. This paper presents a dimensionality reduction of the online calibration problem that is based on principal components to overcome this limitation. To demonstrate this approach, the origin–destination flow estimation problem is formulated in relation to its principal components. The efficacy of the procedure was tested with real data on the Singapore Expressway network in an open-loop framework. A reduction in the problem dimension by a factor of 50 was observed with only a 2% loss in estimation accuracy. Further, the computational times were reduced by an order of 100. The procedure led to better predictions, as the principal components captured the structural spatial relationships. This work has the potential to make the online calibration problem more scalable

Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing

Research in road users’ behaviour typically depends on detailed observational data availability, particularly if the interest is in driving behaviour modelling. Among this type of data, vehicle trajectories are an important source of information for traffic flow theory, driving behaviour modelling, innovation in traffic management and safety and environmental studies. Recent developments in sensing technologies and image processing algorithms reduced the resources (time and costs) required for detailed traffic data collection, promoting the feasibility of site-based and vehicle-based naturalistic driving observation. For testing the core models of a traffic microsimulation application for safety assessment, vehicle trajectories were collected by remote sensing on a typical Portuguese suburban motorway. Multiple short flights over a stretch of an urban motorway allowed for the collection of several partial vehicle trajectories. In this paper the technical details of each step of the methodology used is presented: image collection, image processing, vehicle identification and vehicle tracking. To collect the images, a high-resolution camera was mounted on an aircraft's gyroscopic platform. The camera was connected to a DGPS for extraction of the camera position and allowed the collection of high resolution images at a low frame rate of 2s. After generic image orthorrectification using the flight details and the terrain model, computer vision techniques were used for fine rectification: the scale-invariant feature transform algorithm was used for detection and description of image features, and the random sample consensus algorithm for feature matching. Vehicle detection was carried out by median-based background subtraction. After the computation of the detected foreground and the shadow detection using a spectral ratio technique, region segmentation was used to identify candidates for vehicle positions. Finally, vehicles were tracked using a k- shortest disjoints paths algorithm. This approach allows for the optimization of an entire set of trajectories against all possible position candidates using motion-based optimization. Besides the importance of a new trajectory dataset that allows the development of new behavioural models and the validation of existing ones, this paper also describes the application of state-of-the-art algorithms and methods that significantly minimize the resources needed for such data collection. Keywords: Vehicle trajectories extraction, Driver behaviour, Remote sensin

Volatile Decision Dynamics: Experiments, Stochastic Description, Intermittency Control, and Traffic Optimization

The coordinated and efficient distribution of limited resources by individual decisions is a fundamental, unsolved problem. When individuals compete for road capacities, time, space, money, goods, etc., they normally make decisions based on aggregate rather than complete information, such as TV news or stock market indices. In related experiments, we have observed a volatile decision dynamics and far-from-optimal payoff distributions. We have also identified ways of information presentation that can considerably improve the overall performance of the system. In order to determine optimal strategies of decision guidance by means of user-specific recommendations, a stochastic behavioural description is developed. These strategies manage to increase the adaptibility to changing conditions and to reduce the deviation from the time-dependent user equilibrium, thereby enhancing the average and individual payoffs. Hence, our guidance strategies can increase the performance of all users by reducing overreaction and stabilizing the decision dynamics. These results are highly significant for predicting decision behaviour, for reaching optimal behavioural distributions by decision support systems, and for information service providers. One of the promising fields of application is traffic optimization.Comment: For related work see http://www.helbing.or

Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data

Traffic forecasting has recently attracted increasing interest due to the popularity of online navigation services, ridesharing and smart city projects. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information. To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Specifically, we propose an algorithm to acquire the upcoming traffic volume from an online navigation engine. Taking advantage of the piecewise-linear flow-density relationship, a novel transformer structure converts the upcoming volume into its equivalent in travel time. We combine this signal with the commonly-utilized travel-time signal, and then apply graph convolution to capture the spatial dependency. Particularly, we construct a compound adjacency matrix which reflects the innate traffic proximity. We conduct extensive experiments on real-world datasets. The results show that H-STGCN remarkably outperforms state-of-the-art methods in various metrics, especially for the prediction of non-recurring congestion

Computation of Equilibrium Distributions of Markov Traffic-Assignment Models

Markov traffic-assignment models explicitly represent the day-to-day evolving interaction between traffic congestion and drivers' information acquisition and choice processes. Such models can, in principle, be used to investigate traffic flows in stochastic equilibrium, yielding estimates of the equilibrium mean and covariance matrix of link or route traffic flows. However, in general these equilibrium moments cannot be written down in closed form. While Monte Carlo simulations of the assignment process may be used to produce “empirical” estimates, this approach can be extremely computationally expensive if reliable results (relatively free of Monte Carlo error) are to be obtained. In this paper an alternative method of computing the equilibrium distribution is proposed, applicable to the class of Markov models with linear exponential learning filters. Based on asymptotic results, this equilibrium distribution may be approximated by a Gaussian process, meaning that the problem reduces to determining the first two multivariate moments in equilibrium. The first of these moments, the mean flow vector, may be estimated by a conventional traffic-assignment model. The second, the flow covariance matrix, is estimated through various linear approximations, yielding an explicit expression. The proposed approximations are seen to operate well in a number of illustrative examples. The robustness of the approximations (in terms of network input data) is discussed, and shown to be connected with the “volatility” of the traffic assignment process