Showing papers in "Transportation research procedia in 2017"

Acceleration-Deceleration Behaviour of Various Vehicle Types

Abstract: Acceleration/deceleration (A/D) behaviour of vehicles is important for various applications like length of yellow light at inter- section, determination of sight distances at intersection, determination of length of A/D lanes, ramp design, traffic simulation modelling, vehicular emission modelling, instantaneous fuel consumption rate modelling, etc. Literature reports A/D studies for cars in lane disciplined homogeneous traffic. However, Indian traffic stream is weak lane disciplined and heterogenous, containing various vehicle types like truck, motorized three and two wheeler and diesel and petrol driven cars. Also, the reported studies are based on out of date data, collected using traditional and less accurate methods. Hence, this work aims to study the A/D behaviour of various vehicle types using modern instruments like Global Positioning System (GPS) in controlled manner including maximum A/D envelop. It is observed that acceleration rates of vehicles observed in this study, differed from acceleration rates reported in literature. On finding existing acceleration models insufficient to explain the acceleration behaviour of vehicles observed in this study, new models have been proposed and validated using statistical tools. Acceleration behaviour of cars varied with the change in gears, though the pattern remained similar in all gears.

Road traffic accidents in India: issues and challenges

Abstract: The main aim of this paper is to analyze the road accidents in India at national, state, and metropolitan city level. Analysis shows that the distribution of road accidental deaths and injuries in India varies according to age, gender, month and time. Age group 30-59 years is the most vulnerable population group, though males face higher level of fatalities and injuries than their female counterparts. Moreover, road accidents are relatively higher in extreme weather and during working hours. Analysis of road accident scenario at state and city level shows that there is a huge variation in fatality risk across states and cities. Fatality risk in 16 out of 35 states and union territories is higher than the all India average. Although, burden of road accidents in India is marginally lower in its metropolitan cities, almost 50% of the cities face higher fatality risk than their moffusil counterparts. In general, while in many developed and developing countries including China, road safety situation is generally improving, India faces a worsening situation. Without increased efforts and new initiatives, the total number of road traffic deaths in India is likely to cross the mark of 250,000 by the year 2025. There is thus an urgent need to recognize the worsening situation in road deaths and injuries and to take appropriate action.

Evaluation of Functional Efficiency of Automated Traffic Enforcement Systems

Abstract: Traffic safety is a characteristic feature of road transport systems. Road traffic safety is regarded as a difficult challenge which requires a system approach to the management of the road traffic system and its functional features like variability of the structure of the street and road network and its technical condition, complexity of the hierarchical structure of road transport systems and technologies exploited in them. The research resulted in a model of functioning of automated traffic enforcement facilities and identified the factors which affect the effective functioning of automated traffic enforcement facilities which were used to develop the dependencies regarding the number of accidents to evaluate the effectiveness of traffic enforcement cameras.

Towards an Understanding of the Travel Behavior Impact of Autonomous Vehicles

Abstract: This study gathered empirical evidence on adoption patterns of self-driving vehicles, people's likely use of them, and how that might influence amount of travel, mode choice, auto ownership, and other travel behaviour decisions. Because self-driving vehicles are not yet on the market, a car technology acceptance model was applied to understand adoption and use. Researchers implemented a two-stage data collection effort. An online survey was conducted with 556 residents of metropolitan Austin to determine intent to use. Based on results, four “intent to use” categories were determined: (1) extremely unlikely = Rejecters (18%); (2) somewhat unlikely = Conservatives (32%); (3) somewhat likely = Pragmatists (36%); (4) extremely likely = Enthusiasts (14%). Individuals with a higher level of intent to use have any physical conditions that prohibit them from driving; use technology – smartphone, text messaging, Facebook, transportation apps – and are not concerned with data privacy about using online technology; think using self-driving vehicles would be fun, decrease accident risk, and easy to become skilful at using; and believe people whose opinions are valued would like using them. Among those who indicated intent to use, qualitative interviews were conducted to ascertain the impact on their travel behaviour. Most respondents would rather own self-driving vehicles (59%) than just use one (41%), like a Car2Go or Uber taxi. Additionally, respondents reported that using one would have no change on where people would choose to live in Austin (80%), no change to their annual VMT (66%), and no change to the number of vehicles owned (61%).

The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations

Abstract: Traffic flow oscillations, including traffic waves, are a common yet incompletely understood feature of congested traffic. Possible mechanisms include traffic flow instabilities, indifference regions or finite human perception thresholds (action points), and external acceleration noise. However, the relative importance of these factors in a given situation remains unclear. We bring light into this question by adding external noise and action points to the Intelligent Driver Model and other car-following models thereby obtaining a minimal model containing all three oscillation mechanisms. We show analytically that even in the subcritical regime of linearly stable flow (order parameter ϵ < 0), external white noise leads to spatiotemporal speed correlations “anticipating” the waves of the linearly unstable regime. Sufficiently far away from the threshold, the amplitude scales with (−ϵ)−0.5. By means of simulations and comparisons with experimental car platoons and bicycle traffic, we show that external noise and indifference regions with action points have essentially equivalent effects. Furthermore, flow instabilities dominate the oscillations on freeways while external noise or action points prevail at low desired speeds such as vehicular city or bicycle traffic. For bicycle traffic, noise can lead to fully developed waves even for single-file traffic in the subcritical regime.

Experimental and empirical investigations of traffic flow instability

Abstract: Traffic instability is an important but undesirable feature of traffic flow. This paper reports our experimental and empirical studies on traffic flow instability. We have carried out a large scale experiment to study the car-following behavior in a 51-car-platoon. The experiment has reproduced the phenomena and confirmed the findings in our previous 25-car-platoon experiment, i.e., standard deviation of vehicle speeds increases in a concave way along the platoon. Based on our experimental results, we argue that traffic speed rather than vehicle spacing (or density) might be a better indicator of traffic instability, because vehicles can have different spacing under the same speed. For these drivers, there exists a critical speed between 30 km/h and 40 km/h, above which the standard deviation of car velocity is almost saturated (flat) along the 51-car-platoon, indicating that the traffic flow is likely to be stable. In contrast, below this critical speed, traffic flow is unstable and can lead to the formation of traffic jams. Traffic data from the Nanjing Airport Highway support the experimental observation of existence of a critical speed. Based on these findings, we propose an alternative mechanism of traffic instability: the competition between stochastic factors and the so-called speed adaptation effect, which can better explain the concave growth of speed standard deviation in traffic flow.

Dynamic modeling and control of taxi services in large-scale urban networks: A macroscopic approach

Abstract: Taxis are increasingly becoming a prominent mobility mode in many major cities due to their accessibility and convenience. The growing number of taxi trips and the increasing contribution of taxis to traffic congestion are cause for concern when vacant taxis are not distributed optimally within the city and are unable to find unserved passengers effectively. A way of improving taxi operations is to deploy a taxi dispatch system that matches the vacant taxis and waiting passengers while considering the search friction dynamics. This paper presents a network-scale taxi dispatch model that takes into account the interrelated impact of normal traffic flows and taxi dynamics while optimizing for an effective dispatching system. The proposed model builds on the concept of the macroscopic fundamental diagram (MFD) to represent the dynamic evolution of traffic conditions. The model considers multiple taxi service firms operating in a heterogeneously congested city, where the city is assumed to be partitioned into multiple regions each represented with a well-defined MFD. A model predictive control approach is devised to control the taxi dispatch system. The results show that lack of the taxi dispatching system leads to severe accumulation of unserved taxi passengers and vacant taxis in different regions whereas the dispatch system improves the taxi service performance and reduces traffic congestion by regulating the network towards the undersaturated condition. The proposed framework demonstrates sound potential management schemes for emerging mobility solutions such as fleet of automated vehicles and demand-responsive transit services.

The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment

Abstract: Public administration has adopted information and communication technology in order to construct new intelligent systems and design new risk prevention strategies in transportation management. The ultimate goal is to improve the quality of the transportation services and also to ensure public transportation safety. In this research, a combination of spatial clustering methods and artificial neural network models was used in order to predict the high crime risk transportation areas. Geographic information systems were used to perform spatial analysis so as to identify the regions with a high concentration of crime incidents. Artificial intelligence was used in this study in order to build artificial neural network predictive models. The neural network predictive models were evaluated by using the Mean Squared Error (MSE) in order to find the optimal forecasting model. The optimal forecasting model was used in order to predict the high crime risk transportation areas. The scaled conjugate gradient algorithm was utilized as the training algorithm for the construction of the feedforward neural network models, since it is considered as one of the fastest learning algorithms compared to several other algorithms such as backpropagation learning algorithms.

Impact of congestion on greenhouse gas emissions for road transport in Mumbai metropolitan region

Abstract: Traffic congestion on roads not only increases the fuel consumption but consequently leads to increase in carbon dioxide emissions, outdoor air pollution as well as increase in the exposure time of the passengers. We analyse the greenhouse gas emissions for road transport sector in Mumbai Metropolitan Region (MMR) using fuel consumption and the vehicle kilometre travelled methods. In addition, by conducting traffic survey on four major roads in MMR, the congestion factor was established to estimate the share of greenhouse gas emissions from the road transport sector that can be attributed to traffic congestion.

Autonomous Shared Mobility-On-Demand: Melbourne Pilot Simulation Study

Abstract: This paper presents results from a simulation-based study which aimed to demonstrate the feasibility of using agent-based simulation tools to model the impacts of shared autonomous vehicles First, the paper outlines a research framework for the development and evaluation of low carbon mobility solutions driven by two disruptive forces which are changing the mobility landscape and providing consumers with more choices to meet their transport needs: automated self-driving and on-demand shared mobility services The focus of this paper is on development of rigorous models for understanding the demand for travel in the age of connected mobility, and assessing their impacts particularly under scenarios of autonomous or self-driving on-demand shared mobility To demonstrate the feasibility of the approach, the paper provides initial results from a pilot study on a small road network in Melbourne, Australia A base case scenario representing the current situation of using traditional privately owned vehicles, and two autonomous mobility on-demand (AMoD) scenarios were simulated on a real transport network In the first scenario (AMoD1), it was assumed that the on-demand vehicles were immediately available to passengers (maximum waiting times is zero) This constraint was relaxed in the second scenario (AMoD2) by increasing the allowable passenger waiting times up to a maximum of 5 minutes The results showed that using the AMoD system resulted in a significant reduction in both the number of vehicles required to meet the transport needs of the community (reduction of 43% in AMoD1, and 88% in AMoD2), and the required on-street parking space (reduction of 58% in AMoD1 and 83% in AMoD2) However, the simulation also showed that this was achieved at the expense of a less significant increase in the total VKT (increase of 29% in AMoD1 and 10% in AMoD2) The paper concludes by describing how the model is being extended, the remaining challenges that need to be overcome in this research, and outlines the next steps to achieve the desired outcomes

Smart Mobility and Smart Environment in the Spanish cities

Abstract: Cities play a key role in sustainable development. Urban growth must be managed in ways that support and drive economic development, and achieve social cohesion and environmental sustainability. The concept of Smart Cities emerged in the same way as Smartphones or Smart TVs. A number of initiatives are being developed as part of Smart City projects; however, there is a lack of consistent indicators, databases and methodologies for assessing, financing, and implementing these kinds of initiatives. Smart City projects today are classified according to six clusters known as axes : Mobility, Environment, Government, Economy, People and Living. The main aim of this paper is to show dynamically and graphically the scope of development of Spanish Smart City initiatives in terms of mobility and environmental issues, as two of the fundamental axes of Smart City development. The study was carried out in the 62 cities in the Spanish Smart Cities Network (RECI). The interactive map describes the status of Spanish cities by means of socioeconomic and demographic indicators and provides a thorough assessment of the maturity of Smart Cities based on their variables.

Integrated Public Transport Timetable Synchronization and Vehicle Scheduling with Demand Assignment: A Bi-objective Bi-level Model Using Deficit Function Approach

Abstract: In the operations planning process of public transport (PT), timetable synchronization is a useful strategy utilized to reduce transfer waiting time and improve service connectivity. However, most of the studies on PT timetable synchronization design have treated the problem independently of other operations planning activities, and have focused only on minimizing transfer waiting time. In addition, the impact of schedule changes on PT users’ route/trip choice behavior has not been well investigated yet. This work develops a new bi-objective, bi-level integer programming model, taking into account the interests of PT users and operators in attaining optimization of PT timetable synchronization integrated with vehicle scheduling and considering user demand assignment. Based on the special structure characteristics of the model, a novel deficit function (DF)-based sequential search method combined with network flow and shifting vehicle departure time techniques is proposed to achieve a set of Pareto-efficient solutions. The graphical features of the DF can facilitate a decision-making process for PT schedulers for finding a desirable solution. Two numerical examples are illustrated to demonstrate the methodology developed.

UAV-Based Traffic Analysis: A Universal Guiding Framework Based on Literature Survey

Abstract: The Unmanned Aerial Vehicles (UAVs) commonly also known as drones are considered as one of the most dynamic and multi-dimensional emerging technologies of the modern era. Recently, this technology has found multiple applications in the transportation field as well; ranging from the traffic surveillance applications to the traffic network analysis for the overall improvement of the traffic flow and safety conditions. However, in order to conduct a UAV-based traffic study, an extremely diligent planning and execution is required followed by an optimal data analysis and interpretation procedure. This paper presents a universal guiding framework for ensuring a safe and efficient execution of a UAV-based study. It also explores the analysis steps that follow the execution of a drone flight. The framework based on the existing studies, is classified into the following seven components: (i) scope definition, (ii) flight planning, (iii) flight implementation, (iv) data acquisition, (v) data processing and analysis, (vi) data interpretation and (vii) optimized traffic application. The proposed framework provides a comprehensive guideline for an efficient conduction and completion of a drone-based traffic study. It gives an overview of the management in the context of the hardware and the software entities involved in the process. In this paper, an extensive yet systematic review of the existing traffic-related UAV studies is presented by moulding them in a step-by-step framework. With the significant increase in the number of UAV studies expected in the coming years, this literature review could become a useful resource for future researchers. The future research will mainly focus on the practical applications of the proposed guiding framework of the UAV-based traffic monitoring and analysis study.

Reconstruction of Road Accidents Based on Braking Parameters of Category L3 Vehicles

Abstract: During the last decades, designs of two-wheel motor vehicles (TWMV) have been significantly improved, which is why it is required to bring the analytical tools used to estimate their braking parameters in line with those designs. However, the current expert analysis practice applied to motor vehicle accidents (MVAs) still uses for calculations the values of deceleration time and steady state deceleration for old, domestically manufactured motorcycles which are today rarely used for travel. Both these circumstances require processing, clarification and establishing an updated basis of TWMV deceleration estimate in order to enhance credibility of such estimate used in expert examination of MVAs, i.e. to ascertain the drivers’ compliance or non-compliance with traffic regulations, to justify causes of accidents, to determine whether the driver could or could not have prevented a particular MVA.

Comparative environmental assessment of alternative fueled vehicles using a life cycle assessment

Abstract: Many new vehicle technologies are claiming to be the best in class to reduce the impact on the environment. However what are ‘green’ or ‘clean’ vehicles? How can this be assessed in the appropriate scientific way? The underling assessment compares the environmental aspects of mainly compressed natural gas (CNG) and Battery electric vehicles, along with Liquid Petrol Gas (LPG), Biogas (BG), plug-in hybrid electric vehicles (PHEV), hybrid electric vehicles (HEV) and conventional diesel and petrol vehicles. As an example and context the Brussels Capital region, the centre of Belgium as well as the capital of Europe, has been chosen. The methodology is based on a comparative environmental assessment of vehicle technologies using a life cycle assessment (LCA) approach. However, a special focus is given on the potential of battery electric vehicles to reduce (or increase) the transport related emissions in the Brussels capital region. The results will answer following research questions. What are the environmental impacts (climate change and urban air quality) of these ‘clean’ vehicle technologies compared to conventional vehicles, considering the full life cycle? What is the impact of the electricity production on the total life cycle environmental performance of electric vehicle and how does the type of energy source, to produce electricity, influences the impact? What are the life cycle environmental impacts of battery technologies used in BEVs? What is the effect of BEV charging patterns on the durability of batteries? What are the effects of real world driving styles and charging patterns (peak or off-peak) on the environmental performance of BEVs and PHEVs?

A capacity maximization scheme for intersection management with automated vehicles

Abstract: With the advent of connected and automated vehicle technology, in this paper, we propose an innovative intersection operation scheme named as MCross: M aximum C apacity inte R section O peration S cheme with S ignals. This new scheme maximizes intersection capacity by utilizing all lanes of a road simultaneously. Lane assignment and green durations are dynamically optimized by solving a multi-objective mixed-integer non-linear programming problem. The demand conditions under which full capacity can be achieved in MCross are derived analytically. Numerical examples show that MCross can almost double the intersection capacity (increase by as high as 99.51% in comparison to that in conventional signal operation scheme).

Review of Driving Performance Parameters Critical for Distracted Driving Research

Abstract: While driving simulators allow for the examination of a range of driving performance measures in a controlled, relatively realistic and safe driving environment, driver distraction is a multidimensional phenomenon which means that no single driving performance measure can capture all effects of distraction. Furthermore, the large number of driving related outcomes each simulator provides, indicates that the decision regarding which measure or set of measures is used should be guided by specific criteria. The objective of this paper is a comprehensive review of driving performance parameters critical for distracted driving research. For this purpose an extended literature review took place in order to investigate the critical parameters which are examined in the scientific field of driver distraction. Firstly, all driving performance parameters examined in driving simulator experiments are identified and analysed including lateral control, longitudinal control, reaction time, gap acceptance, eye movement and workload measures, while a list of the most common driving simulator dependent variables is cited. Subsequently, a thorough literature review is carried out including 42 studies examining driver distraction through driving simulator experiments which were published in scientific journals, concern recent research and report quantitative results. In this framework, the respective driving performance measures are recorder aiming to investigate which and how they are analysed. A basic remark concerns the quantitative measures used to express driver distraction. In most cases, driver distraction is measured in terms of its impact to driver attention, driver behaviour and driver accident risk. It is noted that the specific measures used vary significantly. However, the diversity in the measures used, in combination with the diversity in the design of the experiments (i.e. road and traffic factors examined, number and duration of trials) often complicates the synthesis of the results, especially for the less commonly examined distraction factors.

Night-time detection of UAVs using thermal infrared camera

Abstract: With the proliferation of Unmanned Aerial Vehicles (UAVs), a series of safety and security challenges emerged. In recent years there have been numerous safety and security incidents with UAVs which prompted an increase in research of surveillance and interdiction methods tailored for UAVs. Detecting UAVs in flight can become very difficult in some circumstances such as during the night, in low visibility, or in urban environments. Thermal infrared cameras can detect small variations in heat on the level of tens of mK. Electrically powered UAVs do not produce large amounts of heat compared to aircraft powered by fuel combustion. This is because the electric motors are more efficient than combustion engines and because the air around the UAV is rapidly circulated. In this paper we have tested the applicability of a low-cost long-wave infrared sensor for detection of various UAVs in flight.

The effect of transportation on tourism promotion: Literature review on application of the Computable General Equilibrium (CGE) Model

Abstract: This paper reviews the application of computable general equilibrium (CGE) models in empirical studies assessing the impact of transportation factors on tourism. The papers included in this review were searched via Google Scholar, Web of Science, and Scopus, and range in publication date from the first introduction of the CGE model by Johansen in 1960 to articles published in 2015. All of the reviewed studies utilized CGE models to assess the interactions among economic elements, transportation factors, and tourism. The results indicate that, among the 69 papers reviewed: 39 (56%) used CGE models to assess the interaction between tourism and the economy; 24 (35%) used the model to analyze the relationship between transportation and the economy; 4 (6%) focused on the interactions among transportation relevant factors, such as the interaction between oil prices and tourism; and only 2 papers (3%) assessed the direct interaction between transportation and tourism. This paper also argues that integrating transportation accessibility factors into CGE models will be a crucial factor in future research to properly assess the impact of transportation on tourism.

How cities use regulation for innovation: The case of Uber, Lyft and Sidecar in San Francisco

Abstract: How do government actors facilitate or hinder private innovation in urban mobility, and how does local context mediate this relationship? In this paper we examine the regulatory response to on-demand ride services—or “ridesourcing”—through a case study of San Francisco, CA. The entry of Lyft, Sidecar, and UberX in San Francisco in 2012 raised serious questions about the legality of ridesourcing, and sparked significant conflict within regulatory agencies. After sustained debate, regulators decided to welcome the services provided by new companies and crafted a new regulatory framework that legalized the provision of for-profit, on-demand ride services using personal vehicles. We ask, given strong arguments on each side, what motivated public officials in each city to facilitate, rather than hinder, the new services? How did they achieve regulatory reform?

A Multicriteria Analysis for the Green VRP: A Case Discussion for the Distribution Problem of a Spanish Retailer

Abstract: This research presents the group of green vehicle routing problems with environmental costs translated into money versus production of noise, pollution and fuel consumption This research is focused on multi-objective green logistics optimization Optimality criteria are environmental costs: minimization of amount of money paid as externality cost for noise, pollution and costs of fuel versus minimization of noise, pollution and fuel consumption themselves Some mixed integer programming formulations of multi-criteria vehicle routing problems have been considered Mathematical models were formulated under assumption of existence of asymmetric distance-based costs and use of homogeneous fleet The exact solution methods are applied for finding optimal solutions The software used to solve these models is the CPLEX solver with AMPL programming language The researchers were able to use real data from a Spanish company of groceries Problems deal with green logistics for routes crossing the Spanish regions of Navarre, Basque Country and La Rioja Analyses of obtained results could help logistics managers to lead the initiative in area of green logistics by saving money paid for environmental costs as well as direct cost of fuel and minimization of pollution and noise

Total cost of ownership and externalities of conventional, hybrid and electric vehicle

Abstract: While decision makers support the promotion of advanced vehicle types, and incentives are provided to consumers to own and use cleaner vehicles, policies barely rely on the life cycle impacts of transportation vehicles. The increasing number of alternative fuel vehicles urges policy makers to consider life cycle impacts and support dynamic policies to benefit users of cleaner vehicles. To improve the effectiveness of transportation policies a life cycle cost method is created that responds to various vehicle fuels and technologies. A method is developed that integrates indirect costs (externalities), including emissions (i.e., global and local air pollution) and time losses with direct total cost of ownership. Life cycle emissions and time losses are converted into costs for three representative urban light duty vehicles: internal combustion, hybrid and electric vehicle. The results, which are based on vehicle technology characteristics and transportation impacts on environment, facilitate vehicle comparisons and support policy making in transportation.

Analysis of the Electric Vehicles Adoption over the United States

Abstract: Increasing the use of electric vehicles (EVs) has been suggested as a possible method to decrease fuel consumption and greenhouse gas (GHG) emissions in an effort to mitigate the causes of climate change In this study, the relationship between the market share of electric vehicles and the presence of government incentives, and other influential socio-economic factors were examined The methodology of this study is based on a cross-sectional/time-series (panel) analysis The developed model is an aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different US states from 2003 to 2011 The results demonstrated that electricity prices were negatively associated with EV use while urban roads and government incentives were positively correlated with states’ electric vehicle market share Sensitivity analysis suggested that of these factors, electricity price affects electric vehicle adoption rate the most Moreover, the time trend model analysis found that the electric vehicle adoption has been increasing over time, which is consistent with theories about diffusion of new technology

Identification of communities in urban mobility networks using multi-layer graphs of network traffic

Abstract: This paper proposes a novel approach to identify the pockets of activity or the community structure in a city network using multi-layer graphs that represent the movement of disparate entities (i.e. private cars, buses and passengers) in the network. First, we process the trip data corresponding to each entity through a Voronoi segmentation procedure which provides a natural null model to compare multiple layers in a real world network. Second, given nodes that represent Voronoi cells and link weights that define the strength of connection between them, we apply a community detection algorithm and partition the network into smaller areas independently at each layer. The partitioning algorithm returns geographically well connected regions in all layers and reveal significant characteristics underlying the spatial structure of our city. Third, we test an algorithm that reveals the unified community structure of multi-layer networks, which are combinations of single-layer networks coupled through links between each node in one network layer to itself in other layers. This approach allows us to directly compare the resulting communities in multiple layers where connection types are categorically different.

Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options

Abstract: The first analytical stochastic and dynamic model for optimizing transit service switching is proposed for “smart transit” applications and for operating shared autonomous transit fleets. The model assumes a region that requires many-to-one last mile transit service either with fixed-route buses or flexible-route, on-demand buses. The demand density evolves continuously over time as an Ornstein-Uhlenbeck process. The optimal policy is determined by solving the switching problem as a market entry and exit real options model. Analysis using the model on a benchmark computational example illustrates the presence of a hysteresis effect, an indifference band that is sensitive to transportation system state and demand parameters, as well as the presence of switching thresholds that exhibit asymmetric sensitivities to transportation system conditions. The proposed policy is computationally compared in a 24-hour simulation to a “perfect information” set of decisions and a myopic policy that has been dominant in the flexible transit literature, with results that suggest the proposed policy can reduce by up to 72% of the excess cost in the myopic policy. Computational experiments of the “modular vehicle” policy demonstrate the existence of an option premium for having flexibility to switch between two vehicle sizes.

Robust perimeter control for two urban regions with macroscopic fundamental diagrams: A control-Lyapunov function approach

Abstract: The Macroscopic Fundamental Diagram (MFD) framework has been widely utilized to describe traffic dynamics in urban networks as well as to design perimeter flow control strategies under stationary (constant) demand and deterministic settings. In real world, both the MFD and demand however suffer from various intrinsic uncertainties while travel demand is of time-varying nature. Hence, robust control for traffic networks with uncertain MFDs and demand is much appealing and of greater interest in practice. In literature, there would be a lack of robust control strategies for the problem. One major hurdle is of requirement on model linearization that is actually a basis of most existing results. The main objective of this paper is to explore a new robust perimeter control framework for dynamic traffic networks with parameter uncertainty (on the MFD) and exogenous disturbance induced by travel demand. The disturbance in question is in general time-varying and stochastic. Our main contribution focuses on developing a control-Lyapunov function (CLF) based approach to establishing a couple of universal control laws, one is almost smooth and the other is Bang-bang like, for different implementation scenarios. Moreover, it is indicated that the almost smooth control is more suited for road pricing while the Bang-bang like control for signal timing. In sharp contrast to existing methods, in which adjusting extensive design parameters are usually needed, the proposed methods can determine the control in an automatic manner. Furthermore, numerical results demonstrate that the control can drive the system dynamics towards a desired equilibrium under various scenarios with uncertain MFDs and travel demand. Both stability and robustness can be substantially observed. As a major consequence, the proposed methods achieve not only global asymptotic stability but also appealing robustness for the closed-loop traffic system.

Detection of Urban Traffic Patterns from Floating Car Data (FCD)

Abstract: Real time data collection in traffic engineering is crucial for better traffic corridor control and management In the literature, many data collection methods have been used such as; magnetic loops, road tube counters, piezo sensors, radars, Bluetooth etc to estimate the link occupancy, average speed or density of a corridor More recently, Floating Car Data (FCD) has become another important traffic data source and has an increasing usage due to its lower cost and higher coverage despite its reliability problems FCD obtained from GPS equipped vehicles moving in the traffic can provide speed or travel speed data for many segments for even 1-min intervals in real-time Though not totally diverse providing more than one of the traffic flow parameters, measuring the effectiveness of this extensive data source in detecting some critical urban traffic states is the ultimate goal of this study As a case study, 1-min interval FCD for an urban arterial in Ankara has been collected during the morning peak hour for 2 months Average speed values were transformed into a qualitative 4-scale state parameter based on the Level of Service (LOS) definitions for urban roads Pattern searches over consecutive segment states using different search length (ie 2 segments, 3 segments, etc) showed that FCD is capable to detect recurrent congestion or bottleneck locations, and even have an idea about the length of queue formed before the bottlenecks

Applying GIS to Identify the Spatial and Temporal Patterns of Road Accidents Using Spatial Statistics (case study: Ilam Province, Iran)

Abstract: The number of fatalities and casualties caused by road accidents is mostly affected by the 3 factors of road, human, and vehicle. Nowadays, tackling with the accident prone locations including the definition, identification, and modification prioritization has attracted attention as an approach to enhance and improve the roads network safety level. A road accident analysis method is through the use of the Geographic Information System (GIS) and spatial and temporal patterns in accident prone locations. Since accidents are temporal phenomena, in this paper, GIS-based spatial statistical methods have been used to identify and model accident hot spots; in other words, we have investigated the use of localization patterns and hot spot distribution with the help of temporal information.Hot spot analysis with identification and data generation helps decision makers to take appropriate measures to decrease road accidents. To specify and analyze accidents distribution, the information regarding the accidents in the roads of Ilam Province (Iran 2013), has been investigated. Information included the accident type (fatality, injury).From the hot spot map, it is concluded that in northwest roads, despite less traffic, the number (spatial weight) of fatalities is more.This can be due to such factors as the route geometrical design, lack of appropriate relief, and so on. To identify the temporal patterns and accidents distribution, and also analyze the hot spots, Moran's method of spatial autocorrelation and Getis - OrdGi * statistic have been used.

A multi-layer social force approach to model interactions in shared spaces using collision prediction

Abstract: In shared space environments the movements of road users is not regulated by traffic rules, but is the result of spontaneous interaction between traffic users, who negotiate the priority according to social rules such as eye contact or courtesy behavior. However, appropriate micro simulation tools, which can reproduce the operation of shared spaces, are currently lacking. In this paper, a multi-layer approach for representing the movement of road users and their interaction, based on the Social Force Model, is developed. In a free-flow layer a realistic path is calculated for each user towards his destination, while a conflict layer is used for detecting possible conflict situations and computing an appropriate reaction. The novelty of this work in the field of shared space modeling is in the implementation of group dynamics and a SFM based approach for cyclists. The presented approach is qualitatively tested in different traffic situations involving cyclists, pedestrians and pedestrian groups, and shows realistic behavior.

Potentials of Autonomous Vehicles in a Changing Private Transportation System – a Case Study in the Stuttgart Region

Abstract: The extensive market introduction of autonomous vehicles will be revolutionary for traditional transportation systems. Especially today's clear boundaries between private and public transportation systems will blur. Due to the possibility of an autonomous relocation of cars, car-sharing will become more relevant and compete the protected taxicab market in Germany. Private car ownership might even become redundant. Having these possible future circumstances in mind, we use a microscopic travel demand model to simulate the mode choice behaviour in a case study for the Stuttgart region: we presume a world without private cars and the presence of a large autonomous mobility on demand (AMOD) service instead. Following, under the assumption that up to four persons share a ride, we calculated the number of cars needed to run the AMOD service smoothly. We show that not all trips, previously made by private car, are substituted by the AMOD service; the modal share of walking, public transportation and bicycling is increasing as well. Due to lower cost of the AMOD service compared with car trips, trip lengths increase as well. The results show that about 45% of all vehicle movements and 20% of all vehicle kilometres could be saved. Furthermore, the results show that about 85% of all vehicles in the Stuttgart region might be dispensable.