Efforts to improve methods of freight traffic forecasting, to regulate lorry movements in sensitive environments and to rationalise deliveries to shops have been inhibited by limited knowledge of the way products are distributed. This thesis examines the shortcomings of previous methods of freight flow analysis, then proposes an alternative approach which takes much more account of the frameworks of marketing and physical distribution within which freight transport is organised. This approach is then adopted in an investigation of the factors that influence the routeing of food products from factories to shops. This investigation is based on data collected in surveys of manufacturers, multiple retailers, wholesalers and distribution contractors, and drawn from various published sources. Consideration is given first to the allocation of food manufacturers' output between different marketing channels. This determines the number and nature of agencies handling this output. Of these agencies, the manufacturer and multiple retailer generally have a choice of logistical channel, i.e. they can either transport goods directly or channel them through an intermediate stockholding/ transhipment point. The research examines the factors influencing the choice of logistical channel and the nature of the link between channels controlled by food manufacturers and retailers. The spatial structure of these logistical channels is also explored, particularly in terms of the number and locations of intervening nodes between factory and shop. Later sections of the thesis investigate the routeing of flows through this framework of distributive nodes. A distinction is made between the 'strategic' routeing of bulk movements between factories and depots, and the more localised 'tactical' routeing of deliveries to shops. At each stage, attempts are made to explain variations in the spatial organization of firms' distribution operations and to establish general relationships between distribution variables. Data on the present state and recent development of the food distribution system are used to help to explain trends in general freight statistics. The thesis concludes with an assessment of the advantages and limitations of this approach and consideration of the implications of the research findings for the way in which freight traffic is forecast and regulated.

There is a recent surge in the development of spatio-temporal forecasting models in many applications, and traffic forecasting is one of the most important ones. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in traffic networks. Current works primarily rely on road networks with graph structures and learn representations using graph neural networks (GNNs), but this approach suffers from over-smoothing problem in deep architectures. To tackle this problem, recent methods introduced the combination of GNNs with residual connections or neural ordinary differential equations (NODEs). The existing graph ODE models are still limited in feature extraction due to (1) having bias towards global temporal patterns and ignoring local patterns which are crucial in case of unexpected events; (2) missing dynamic semantic edges in the model architecture; and (3) using simple aggregation layers that disregard the high-dimensional feature correlations. In this thesis, we propose a novel architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) which is designed with multiple connective ODE-GNN modules to learn better representations by capturing different views of complex local and global dynamic spatio-temporal dependencies. We also add some techniques to further improve the communication between different ODE-GNN modules towards the forecasting task. Extensive experiments conducted on four real-world datasets demonstrate the outperformance of GRAM-ODE compared with state-of-the-art baselines as well as the contribution of different GRAM-ODE components to the performance. / Master of Science / There is a recent surge in the development of spatio-temporal forecasting models in many applications, and traffic forecasting is one of the most important ones. In traffic forecasting, current works limited in correctly capturing the key correlation of spatial and temporal patterns. In this thesis, we propose a novel architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) to tackle the problem by using the separate ODE modules to deal with spatial and temporal patterns and further improve the communication between different modules. Extensive experiments conducted on four real-world datasets demonstrate the outperformance of GRAM-ODE compared with state-of-the-art baselines.

Intelligent transportation systems (ITS) are essential tools for traffic planning, analysis, and forecasting that can utilize the huge amount of traffic data available nowadays. In this work, we aggregated detailed traffic flow sensor data, Waze reports, OpenStreetMap (OSM) features, and weather data, from California Bay Area for 6 months. Using that data, we studied three novel ITS applications using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first experiment is an analysis of the relation between roadway shapes and accident occurrence, where results show that the speed limit and number of lanes are significant predictors for major accidents on highways. The second experiment presents a novel method for forecasting congestion severity using crowdsourced data only (Waze, OSM, and weather), without the need for traffic sensor data. The third experiment studies the improvement of traffic flow forecasting using accidents, number of lanes, weather, and time-related features, where results show significant performance improvements when the additional features where used.

Small unmanned vehicles are used to provide the eye-in-the-sky alternative to monitoring and regulating traffic dynamically. Spatial-temporal visual data are collected in real-time and they are used to generate traffic-related statistical profiles, serving as inputs to traffic simulation models. Generated profiles, which are continuously updated, are used to calibrate traffic model parameters, to obtain more accurate and reliable simulation models, and for model modifications. This method overcomes limitations of existing traffic simulation models, which suffer from outdated data, poorly calibrated parameters, questionable accuracy and poor predictions of traffic patterns.

As transportation funding becomes increasingly constrained, it is imperative that decision makers invest precious resources wisely and effectively. Transportation planners need effective tools for anticipating outcomes (or ranges of outcomes) in order to select preferred project alternatives and evaluate funding options for competing projects. To this end, this thesis work describes multiple applications of a new Project Evaluation Toolkit (PET) for highway project assessment. The PET itself was developed over a two-year period by the thesis author, in conjunction with Dr. Kara Kockelman, Dr. Chi Xie, and some support by others, as described in Kockelman et al. (2010) and the PET Users Guidebook (Fagnant et al. 2011). Using just link-level traffic counts (and other parameter values, if users wish to change defaults), PET quickly estimates how transportation network changes impact traveler welfare (consisting of travel times and operating costs), travel time reliability, crashes, and emissions. Summary measures (such as net present values and benefit-cost ratios) are developed over multi-year/long-term horizons to quantify the relative merit of project scenarios. This thesis emphasizes three key topics: a background and description of PET, case study evaluations using PET, and sensitivity analysis (under uncertain inputs) using PET. The first section includes a discussion of PET’s purpose, operation and theoretical behavior, much of which is taken from Fagnant et al. (2010). The second section offers case studies on capacity expansion, road pricing, demand management, shoulder lane use, speed harmonization, incident management and work zone timing along key links in the Austin, Texas network. The final section conducts extensive sensitivity testing of results for two competing upgrade scenarios (one tolled, the other not); the work examines how input variations impact PET outputs over hundreds of model applications. Taken together, these investigations highlight PET’s capabilities while identifying potential shortcomings. Such findings allow transportation planners to better appreciate the impacts that various projects can have on the traveling public, how project evaluation may best be tackled, and how they may use PET to anticipate impacts of projects they may be considering, before embarking on more detailed analyses and finalizing investment decisions. / text

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Porozumění chování silniční dopravy je klíčem pro její efektivní řízení a organizaci. Tato úloha se stává čím dál více důležitou s rostoucími požadavky na dopravu a počtem registrovaných vozidel. Informace o dopravní situaci je důležitá pro řidiče a osoby zodpovědné za její řízení. Naštěstí v posledních několika dekádách došlo k značnému rozvoji technologií pro monitorování dopravní situace. Stacionární senzory, jako jsou indukční smyčky, radary, kamery a infračervené senzory, mohou být nainstalovány na důležitých místech. Zde jsou schopny měřit různé mikroskopické a makroskopické dopravní veličiny. Bohužel mnohá měření obsahují nekorektní data, která není možné použít při dalším zpracování, například pro predikci dopravy a její inteligentní řízení. Tato nekorektní data mohou být způsobena poruchou zařízení nebo problémy při přenosu dat. Z tohoto důvodu je důležité navrhnout obecný framework, který je schopný doplnit chybějící data. Navíc by tento framework měl být také schopen poskytovat krátkodobou predikci budoucího stavu dopravy. Tato práce se především zabývá vybranými problémy v oblasti doplnění chybějících dopravních dat, predikcí dopravy v krátkém časovém horizontu a predikcí dojezdových dob. Navrhovaná řešení jsou založena na kombinaci současných metod strojového učení, například Support vector regression (SVR) a multikriteriálních evolučních algoritmů. SVR má mnoho meta-parametrů, které je nutné dobře nastavit tak, aby byla dosažena co nejkvalitnější predikce. Kvalita predikce SVR dále silně závisí na výběru vhodné množiny vstupních proměnných. V této práci používáme multiktriteriální optimalizaci pro optimalizaci SVR meta-parametrů a množiny vstupních proměnných. Multikriteriální optimalizace nám umožňuje získat mnoho Pareto nedominovaných řešení. Mezi těmito řešeními je možné dynamicky přepínat dle toho, jaká data jsou aktuálně k dispozici tak, aby bylo dosaženo maximální kvality predikce. Metody navržené v této práci jsou především vhodné pro prostředí s velkým množstvím chybějících hodnot v dopravních datech. Tyto metody jsme ověřili na reálných datech a porovnali jejich výsledky s metodami, které jsou v současné době používány. Navržené metody poskytují lepší výsledky než stávající metody, a to především ve scénářích, kde se vyskytuje mnoho chybějících hodnot v dopravních datech.

Despite the significant investments over the last few decades to enhance and improve road infrastructure worldwide, the capacity of road networks has not kept pace with the ever increasing growth in demand. As a result, congestion has become endemic to many highways and city streets. As an alternative to costly and sometimes infeasible construction of new roads, transportation departments are increasingly looking at ways to improve traffic flow over the existing infrastructure. The biggest challenge in accomplishing this goal is the ability to sample traffic data, estimate traffic current state, and forecast its future behavior. In this thesis, we first address the problem of traffic sampling where we propose strategies for frugal sensing where we collect a fraction of the observed traffic information to reduce costs while achieving high accuracy. Next we demonstrate how traffic estimation using deterministic traffic models can be improved using proposed data reconstruction techniques. Finally, we propose how mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function can improve short-term and long-term traffic forecasting. As mobile devices become more pervasive, participatory sensing is becoming an attractive way of collecting large quantities of valuable location-based data. An important participatory sensing application is traffic monitoring, where GPS-enabled smartphones can provide invaluable information about traffic conditions. We propose a strategy for frugal sensing in which the participants send only a fraction of the observed traffic information to reduce costs while achieving high accuracy. The strategy is based on autonomous sensing, in which participants make decisions to send traffic information without guidance from the central server, thus reducing the communication overhead and improving privacy. To provide accurate and computationally efficient estimation of the current traffic, we propose to use a budgeted version of the Gaussian Process model on the server side. The experiments on real-life traffic data sets indicate that the proposed approach can use up to two orders of magnitude less samples than a baseline approach with only a negligible loss in accuracy. The estimation of the state of traffic provides a detailed picture of the conditions of a traffic network based on limited traffic measurements and, as such, plays a key role in intelligent transportation systems. Most often, traffic measurements are aggregated over multiple time steps, and this procedure raises the question of how to best use this information for state estimation. Reconstructing the high-resolution measurements from the aggregated ones and using them to correct the state estimates at every time step are proposed. Several reconstruction techniques from signal processing, including kernel regression and a reconstruction approach based on convex optimization, were considered. Experimental results show that signal reconstruction leads to more accurate traffic state estimation as compared with the standard approach for dealing with aggregated measurements. Accurate traffic speed forecasting can help in trip planning by allowing travelers to avoid congested routes, either by choosing alternative routes or by changing the departure time. An important feature of traffic is that it consists of free flow and congested regimes, which have significantly different properties. Training a single traffic speed predictor for both regimes typically results in suboptimal accuracy. To address this problem, a mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function was developed. Experimental results showed that mixture of experts approach outperforms several popular benchmark approaches. / Computer and Information Science

Yes / Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.

Annual Average Daily Traffic (AADT) data in the transportation industry today is an important tool used in various fields such as highway planning, pavement design, traffic safety, transport operations, and policy-making/analyses. Systematic literature review was used to identify the current methods of estimating AADT and ranked. Ordinary linear kriging occurred most. Also, factors that influence the accuracy of AADT estimation methods as identified include geographical location and road type amongst others. In addition, further analysis was carried out to determine the most apposite kriging algorithm for AADT data. Three linear (universal, ordinary, and simple), three nonlinear (disjunctive, probability, and indicator) and bayesian (empirical bayesian) kriging methods were compared. Spherical and exponential models were employed as the experimental variograms to aid the spatial interpolation and cross-validation. Statistical measures of correctness (mean prediction and root-mean-square errors) were used to compare the kriging algorithms. Empirical bayesian with exponential model yielded the best result.