Planning Processes for Transport Solutions An Application To the City of Bologna

Mathematical models and the involved methods applied to real contexts are essential tools for designing and evaluating solutions concerning physical elements and/or organizational components of transportation systems. To deal with this, the systems engineering approach is used, which considers the relationships among the transportation system elements and their performances. This approach allows quantifying the effects of transportation projects by taking into account the intrinsic complexity of the transportation system and then assessing the effects of solutions to solve – or mitigate – transportation problems. This thesis focuses on the application of the transport system engineering approach to a real city – Bologna, in northern Italy – in order to: 1. simulate the current transportation system conditions (status quo); 2. compare and assess the results obtained by two different approaches for simulating the link traffic flows on the road transportation network and their related impacts (externalities) 3. identify potential solutions to solve critical aspects, particularly in terms of traffic flow congestion and related environmental impacts (findings)

Comparasion of road traffic emission models in Madrid (Spain)

Many cities in Europe have difficulties to meet the air quality standards set by the European legislation, most particularly the annual mean Limit Value for NO2. Road transport is often the main source of air pollution in urban areas and therefore, there is an increasing need to estimate current and future traffic emissions as accurately as possible. As a consequence, a number of specific emission models and emission factors databases have been developed recently. They present important methodological differences and may result in largely diverging emission figures and thus may lead to alternative policy recommendations. This study compares two approaches to estimate road traffic emissions in Madrid (Spain): the COmputer Programme to calculate Emissions from Road Transport (COPERT4 v.8.1) and the Handbook Emission Factors for Road Transport (HBEFA v.3.1), representative of the ‘average-speed’ and ‘traffic situation’ model types respectively. The input information (e.g. fleet composition, vehicle kilometres travelled, traffic intensity, road type, etc.) was provided by the traffic model developed by the Madrid City Council along with observations from field campaigns. Hourly emissions were computed for nearly 15 000 road segments distributed in 9 management areas covering the Madrid city and surroundings. Total annual NOX emissions predicted by HBEFA were a 21% higher than those of COPERT. The discrepancies for NO2 were lower (13%) since resulting average NO2/NOX ratios are lower for HBEFA. The larger differences are related to diesel vehicle emissions under “stop & go” traffic conditions, very common in distributor/secondary roads of the Madrid metropolitan area. In order to understand the representativeness of these results, the resulting emissions were integrated in an urban scale inventory used to drive mesoscale air quality simulations with the Community Multiscale Air Quality (CMAQ) modelling system (1 km2 resolution). Modelled NO2 concentrations were compared with observations through a series of statistics. Although there are no remarkable differences between both model runs, the results suggest that HBEFA may overestimate traffic emissions. However, the results are strongly influenced by methodological issues and limitations of the traffic model. This study was useful to provide a first alternative estimate to the official emission inventory in Madrid and to identify the main features of the traffic model that should be improved to support the application of an emission system based on “real world” emission factors.

Understanding how to reduce road transport emissions : modelling the impact of eco-driving

Entre los problemas medioambientales más trascendentales para la sociedad, se encuentra el del cambio climático así como el de la calidad del aire en nuestras áreas metropolitanas. El transporte por carretera es uno de los principales causantes, y como tal, las administraciones públicas se enfrentan a estos problemas desde varios ángulos: Cambios a modos de transporte más limpios, nuevas tecnologías y combustibles en los vehículos, gestión de la demanda y el uso de tecnologías de la información y la comunicación (ICT) aplicadas al transporte. En esta tesis doctoral se plantea como primer objetivo el profundizar en la comprensión de cómo ciertas medidas ICT afectan al tráfico, las emisiones y la propia dinámica de los vehículos. El estudio se basa en una campaña de recogida de datos con vehículos flotantes para evaluar los impactos de cuatro medidas concretas: Control de velocidad por tramo, límites variables de velocidad, limitador de velocidad (control de crucero) y conducción eficiente (eco‐driving). Como segundo objetivo, el estudio se centra en la conducción eficiente, ya que es una de las medidas que más ahorros de combustible presenta a nivel individual. Aunque estas reducciones están suficientemente documentadas en la literatura, muy pocos estudios se centran en estudiar el efecto que los conductores eficientes pueden tener en el flujo de tráfico, y cuál sería el impacto si se fuera aumentando el porcentaje de este tipo de conductores. A través de una herramienta de microsimulación de tráfico, se han construido cuatro modelos de vías urbanas que se corresponden con una autopista urbana, una arteria, un colector y una vía local. Gracias a los datos recogidos en la campaña de vehículos flotantes, se ha calibrado el modelo, tanto el escenario base como el ajuste de parámetros de conducción para simular la conducción eficiente. En total se han simulado 72 escenarios, variando el tipo de vía, la demanda de tráfico y el porcentaje de conductores eficientes. A continuación se han calculado las emisiones de CO2 and NOx mediante un modelo de emisiones a nivel microscópico. Los resultados muestran que en escenarios con alto porcentaje de conductores eficientes y altas demandas de tráfico las emisiones aumentan. Esto se debe a que las mayores distancias de seguridad y las aceleraciones y frenadas suaves hacen que aumente la congestión, produciendo así mayores emisiones a nivel global. Climate change and the reduced air quality in our metropolitan areas are two of the main environmental problems that the society is addressing currently. Being road transportation one of the main contributors, public administrations are facing these problems from different points of view: shift to cleaner modes, new fuels and vehicle technologies, demand management and the use of information and communication technologies (ICT) applied to transportation. The first objective of this thesis is to understand how certain ICT measures affect traffic, emissions and vehicle dynamics. The study is based on a data collection campaign with floating vehicles to evaluate the impact of four specific measures: section speed control, variable speed limits, cruise control and eco‐driving. The second objective of the study focuses on eco‐driving, as it is one of the measures that present the largest fuel savings at an individual level. Although these savings are well documented in the literature, few studies focus on how ecodrivers affect the surrounding vehicles and the traffic, and what would be the impact in case of different eco‐drivers percentage. Using a traffic micro‐simulation tool, four models in urban context have been built, corresponding to urban motorway, urban arterial, urban collector and a local street. Both the base‐case and the parameters setting to simulate eco‐driving have been calibrated with the data collected through floating vehicles. In total 72 scenarios were simulated, varying the type of road, traffic demand and the percentage of eco‐drivers. Then, the CO2 and NOx emissions have been estimated through the use of an emission model at microscopic level. The results show that in scenarios with high percentage of co‐drivers and high traffic demand the emissions rise. Higher headways and smooth acceleration and decelerations increase congestion, producing higher emissions globally.

A static traffic assignment model combined with an artificial neural network delay model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Urban air mobility: modelling the future transport system

The advances in the aviation field, particularly the development of electric flying vehicles, as UAV and eVTOL, paved the way for setting Urban Air Mobility (UAM) services. UAM would provide services for passengers, goods and emergencies and could offer faster trips than ground ones. It is expected that early UAM operations will be performed at Very Low-Level airspace as 0-500 m Above Ground Level. The purpose of this research is to both explore the main features of UAM and test an aerial network model, which could be integrated in a multimodal transport system where ground and aerial mobility services are provided. Analyses on UAM transport system involved two sub-systems: the transport demand sub-system, i.e., the mobility requirements, and the transport supply sub-system, i.e., the service and facilities enabling mobility. At first, the UAM demand levels and features for an Airport Shuttle service have been explored through a suitable survey, by combining Revealed and Stated Preference methodologies, and by calibrating some discrete mode choice models. Then, the focus has been on the transport supply model for UAM services, by focusing on both the ground access points (vertiports) and the aerial network model. A suitable three-dimensional urban aerial network (3D-UAN) model that could support fast aerial connections between O/D pairs has been proposed. Some tests have been implemented to verify the feasibility of the proposed model. Some flying vehicles supporting an Airport Shuttle service have been simulated on the aerial network, which has been specified in terms of both topological features and link transport costs. The preliminary results have showed that the proposed 3D-UAN model could be suitable for supporting UAM services. As for transport engineering, the UAM system framework proposed in this thesis paves the way for further research on air-ground multimodality in urban areas.

Risk-based modelling of diffuse land use impacts from rural landscapes upon salmonid fry abundance

Research has demonstrated that landscape or watershed scale processes can influence instream aquatic ecosystems, in terms of the impacts of delivery of fine sediment, solutes and organic matter. Testing such impacts upon populations of organisms (i.e. at the catchment scale) has not proven straightforward and differences have emerged in the conclusions reached. This is: (1) partly because different studies have focused upon different scales of enquiry; but also (2) because the emphasis upon upstream land cover has rarely addressed the extent to which such land covers are hydrologically connected, and hence able to deliver diffuse pollution, to the drainage network However, there is a third issue. In order to develop suitable hydrological models, we need to conceptualise the process cascade. To do this, we need to know what matters to the organism being impacted by the hydrological system, such that we can identify which processes need to be modelled. Acquiring such knowledge is not easy, especially for organisms like fish that might occupy very different locations in the river over relatively short periods of time. However, and inevitably, hydrological modellers have started by building up piecemeal the aspects of the problem that we think matter to fish. Herein, we report two developments: (a) for the case of sediment associated diffuse pollution from agriculture, a risk-based modelling framework, SCIMAP, has been developed, which is distinct because it has an explicit focus upon hydrological connectivity; and (b) we use spatially distributed ecological data to infer the processes and the associated process parameters that matter to salmonid fry. We apply the model to spatially distributed salmon and fry data from the River Eden, Cumbria, England. The analysis shows, quite surprisingly, that arable land covers are relatively unimportant as drivers of fry abundance. What matters most is intensive pasture, a land cover that could be associated with a number of stressors on salmonid fry (e.g. pesticides, fine sediment) and which allows us to identify a series of risky field locations, where this land cover is readily connected to the river system by overland flow. (C) 2010 Elsevier B.V. All rights reserved.

Management of Public Road Transport System in Trivandrum City

An efficient passenger road transport system is a boon to any city and an inefficient one its bane. Passenger bus transport operation involves various aspects like passenger convenience, profitability of operation and social, technological and environmental factors. The author’s interest in this area was aroused when he conducted a traffic survey of Trivandrum City in 1979. While some studies on the performance of the Kerala State Road Transport Corporation in specific areas like finance, inventory control etc. have already been made, no study has been made from the operational point of view. The study is also the first one of its kind in dealing with the transportation problems for a second order city like Trivandrum. The objective of this research study is to develop a scientific basis for analysing and understanding the various operational aspects of urban bus transport management like assessing travel demand, depot location, fleet allocation, vehicle scheduling, maintenance etc. The operation of public road transportation in Trivandrum City is analysed on the basis of this theoretical background. The studies made have relevance to any medium sized city in India or even abroad. If not properly managed, deterioration of any public utility system is a natural process and it adversely affects the consumers, the economy and the nation. Making any system more efficient requires careful analysis, judicious decision making and proper implementation. It is hoped that this study will throw some light into the various operational aspects of urban passenger road transport management which can be of some help to make it perform more efficiently

Transportation theory and planning

The aim of this research is to analyze the transport system and its subcomponents in order to highlight which are the design tools for physical and/or organizational projects related to transport supply systems. A characteristic of the transport systems is that the change of their structures can recoil on several entities, groups of entities, which constitute the community. The construction of a new infrastructure can modify both the transport service characteristic for all the user of the entire network; for example, the construction of a transportation infrastructure can change not only the transport service characteristics for the users of the entire network in which it is part of, but also it produces economical, social, and environmental effects. Therefore, the interventions or the improvements choices must be performed using a rational decision making approach. This approach requires that these choices are taken through the quantitative evaluation of the different effects caused by the different intervention plans. This approach becomes even more necessary when the decisions are taken in behalf of the community. Then, in order to understand how to develop a planning process in Transportation I will firstly analyze the transport system and the mathematical models used to describe it: these models provide us significant indicators which can be used to evaluate the effects of possible interventions. In conclusion, I will move on the topics related to the transport planning, analyzing the planning process, and the variables that have to be considered to perform a feasibility analysis or to compare different alternatives. In conclusion I will perform a preliminary analysis of a new transit system which is planned to be developed in New York City.

Impact of introducing longer and heavier vehicles on regional consumer price index and Spanish road freight transport system

This paper applies an integrated modeling approach to the case of Spain; the approach is based on a random utility-based multiregional input-output model and a road transport network model for assessing the effect of introducing longer and heavier vehicles (LHVs) on the regional consumer price index (CPI) and on the transportation system. The approach strongly supports the concept that changes in transport costs derived from the LHV allowance as well as the economic structure of regions have direct and indirect effects on the economy and on the transportation system. Results show that the introduction of LHVs might reduce prices paid by consumers for a representative basket of goods and services in the regions of Spain and would also lead to a reduction in the regional CPI. In addition, the magnitude and extent of changes in the transportation system are estimated by using the commodity-based structure of the approach to identify the effect of traffic changes on traffic flows and on pollutant emissions over the whole network.

An electronic surveillance and control system for traffic management of the Borman expressway. Final report.

Indiana Department of Transportation, Indianapolis

Incorporating variable peak -to -daily ratios in the equilibrium traffic assignment procedure of the Florida Standard Urban Transportation Modeling Structure (FSUTMS)

The standard highway assignment model in the Florida Standard Urban Transportation Modeling Structure (FSUTMS) is based on the equilibrium traffic assignment method. This method involves running several iterations of all-or-nothing capacity-restraint assignment with an adjustment of travel time to reflect delays encountered in the associated iteration. The iterative link time adjustment process is accomplished through the Bureau of Public Roads (BPR) volume-delay equation. Since FSUTMS' traffic assignment procedure outputs daily volumes, and the input capacities are given in hourly volumes, it is necessary to convert the hourly capacities to their daily equivalents when computing the volume-to-capacity ratios used in the BPR function. The conversion is accomplished by dividing the hourly capacity by a factor called the peak-to-daily ratio, or referred to as CONFAC in FSUTMS. The ratio is computed as the highest hourly volume of a day divided by the corresponding total daily volume. ^ While several studies have indicated that CONFAC is a decreasing function of the level of congestion, a constant value is used for each facility type in the current version of FSUTMS. This ignores the different congestion level associated with each roadway and is believed to be one of the culprits of traffic assignment errors. Traffic counts data from across the state of Florida were used to calibrate CONFACs as a function of a congestion measure using the weighted least squares method. The calibrated functions were then implemented in FSUTMS through a procedure that takes advantage of the iterative nature of FSUTMS' equilibrium assignment method. ^ The assignment results based on constant and variable CONFACs were then compared against the ground counts for three selected networks. It was found that the accuracy from the two assignments was not significantly different, that the hypothesized improvement in assignment results from the variable CONFAC model was not empirically evident. It was recognized that many other factors beyond the scope and control of this study could contribute to this finding. It was recommended that further studies focus on the use of the variable CONFAC model with recalibrated parameters for the BPR function and/or with other forms of volume-delay functions. ^

Development of a real time Image analysis system for traffic monitoring applications.

Image processing offers unparalleled potential for traffic monitoring and control. For many years engineers have attempted to perfect the art of automatic data abstraction from sequences of video images. This paper outlines a research project undertaken at Napier University by the authors in the field of image processing for automatic traffic analysis. A software based system implementing TRIP algorithms to count cars and measure vehicle speed has been developed by members of the Transport Engineering Research Unit (TERU) at the University. The TRIP algorithm has been ported and evaluated on an IBM PC platform with a view to hardware implementation of the pre-processing routines required for vehicle detection. Results show that a software based traffic counting system is realisable for single window processing. Due to the high volume of data required to be processed for full frames or multiple lanes, system operations in real time are limited. Therefore specific hardware is required to be designed. The paper outlines a hardware design for implementation of inter-frame and background differencing, background updating and shadow removal techniques. Preliminary results showing the processing time and counting accuracy for the routines implemented in software are presented and a real time hardware pre-processing architecture is described.

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.