The use of patent analysis in assessing ITS innovations: US,Europe and Japan

A wealth of research on ITS has been carried out for the past two decades. In view of the rapid technological development and innovations occurring in ITS, the ITS stakeholders need to be aware of the usefulness and availability of patents associated with ITS technology. Among the national and international reviews which have been made available in journals of record and freely available public sources, little use has been made of patent information in assessing ITS innovations. Therefore, the paper looks at the ITS technology developments from the international perspective through utilizing rich, reliable information provided by ITS-related invention patents in the world’s three leading patent databases (EPO, USPTO, and JPO) to provide an overall picture of ITS innovations. The research findings show that the patent analysis can provide firms involved in the ITS sector and ITS researchers with information that can assist in their strategic planning efforts.     

Estimating vehicle miles traveled combined with ITS data

‘Vehicle miles traveled’ (VMT) is an important performance measure for highway systems. Currently, VMT [or ‘annual average daily traffic’ (AADT)] is estimated from a combination of permanent counting stations and short-term counts done at specified locations as part of the Highway Performance Monitoring System (HPMS) mandated by the US Federal Highway Administration. However, on some roadway sections, Intelligent Transportation Systems (ITS) such as detectors and cameras also produce traffic data. The question addressed in this paper is whether and under what conditions ITS systems data could be used instead of HPMS short-term counts (called ‘coverage counts’)? This paper develops a methodology for determining a threshold number of missing daily traffic counts, or alternatively, the number of valid ITS data observations needed, in order to confidently replace the HPMS coverage counts with ITS data.

Because ITS counts, coverage counts, and actual ground counts (e.g. continuous counts) cannot be found coexisting on a roadway section, it is hard to compare them directly. In this paper, the Monte Carlo simulation method is employed to generate synthetic ITS counts and coverage counts from a set of relatively complete traffic counts collected at a continuous count station. Comparisons are made between simulated ITS counts, coverage counts, and actual ground counts. The simulation results indicate that when there are<330 daily traffic counts missing in a set of ITS counts in a year, that is, when there are at least 35 days of valid data, ITS counts can be used to derive a better AADT than using coverage counts. This result is applied to calculate the VMT for the Hampton Roads region in Virginia. The comparison between the VMTs derived with using and not using the threshold number indicates that these two VMTs are significantly different.     

Current map-matching algorithms for transport applications: State-of-the art and future research directions

Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines) to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link. A map-matching algorithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS). The required horizontal positioning accuracy of such ITS applications is in the range of 1 m to 40 m (95%) with relatively stringent requirements placed on integrity (quality), continuity and system availability. A number of map-matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting ITS applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements. The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also highlights the potential impacts of the forthcoming European Galileo system and the European Geostationary Overlay Service (EGNOS) on the performance of map matching algorithms. Although not addressed in detail, the paper also presents some ideas for monitoring the integrity of map-matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.     

An integrated framework for embedding large-scale dynamic traffic assignment models in advanced traveler information systems

In recent years, increasing attention has been drawn to the development of various applications of intelligent transportation systems (ITS), which are credited with the amelioration of traffic conditions in urban and regional environments. Advanced traveler information systems (ATIS) constitute an important element of ITS by providing potential travelers with information on the network's current performance both en-route and pre-trip. In order to tackle the complexity of such systems, derived from the difficulty of providing real-time estimations of current as well as forecasts of future traffic conditions, a series of models and algorithms have been initiated. This paper proposes the development of an integrated framework for real-time ATIS and presents its application on a large-scale network, that of Thessaloniki, Greece, concluding with a discussion on development and implementation challenges as well as on the advantages and limitations of such an effort.     

Machine learning for multi-jurisdictional optimal traffic corridor control

Urban traffic corridors are often controlled by more than one agency. Typically in North America, a state of provincial transportation department controls freeways while another agency at the municipal or city level controls the nearby arterials. While the different segments of the corridor fall under different jurisdictions, traffic and users know no boundaries and expect seamless service. Common lack of coordination amongst those authorities due to lack of means for information exchange and/or possible bureaucratic ‘institutional grid-lock’ could hinder the full potential of technically-possible integrated control. Such institutional gridlock and related lack of timely coordination amongst the different agencies involved can have a direct impact on traffic gridlock. One potential solution to this problem is through integrated automatic control under intelligent transportation systems (ITS). Advancements in ITS and communication technology have the potential to considerably reduce delay and congestion through an array of network-wide traffic control and management strategies that can seamlessly cross-jurisdictional boundaries. Perhaps two of the most promising such control tools for freeway corridors are traffic-responsive ramp metering and/or dynamic traffic diversion possibly using variable message signs (VMS). Technically, the use of these control methods separately might limit their potential usefulness. Therefore, integrated corridor control using ramp metering and VMS diversion simultaneously might be synergetic and beneficial. Motivated by the above problem and potential solution approach, the aim of the research presented in this paper is to develop a self-learning adaptive integrated freeway-arterial corridor control for both recurring and non-recurring congestion. The paper introduces the use of reinforcement learning, an Artificial Intelligence method for machine learning, to provide optimal control using ramp metering and VMS routing in an integrated agent for a freeway-arterial corridor. Reinforcement learning is an approach whereby the control agent directly learns optimal strategies via feedback reward signals from its environment. A simple but powerful reinforcement learning method known as Q-learning is used. Results from an elaborate simulation study on a key corridor in Toronto are very encouraging and discussed in the paper.     

New routes on old railways: increasing rail’s mode share within the constraints of the existing railway network

This paper describes an integrated methodology for identifying potential ‘quick wins’ for mode shift from road to passenger rail transport. Firstly, a procedure for analysing rail’s relative competitiveness in the market for passenger transport between large urban areas is developed and then applied to a UK case study. The purpose of such analysis is to allow the identification of flows where rail is currently relatively uncompetitive (in terms of journey time in particular) and to assess the reasons for this poor performance, so that the issues which suppress rail use may be addressed. In parallel, a framework, methodology and tool for the assessment of existing and potential capacity (trains, seats, TEUs, etc.) is developed for both passenger and freight traffic, to identify and address network constraints. An illustrative example of the use of these demand and capacity assessment tools is then presented, with the tools used to identify and evaluate flows where rail demand is suppressed by poor service quality and where spare capacity exists which would allow the passenger rail service to be improved without requiring significant investments in infrastructure. The effects of such improvements on demand are predicted, and the cost implications of operating such additional services are discussed. The analysis suggests that there may be significant potential for increasing rail’s mode share by providing additional inter-urban services where rail currently offers an inferior service.     

A monopolistic market for advanced traveller information systems and road use efficiency

Advanced traveller information systems (ATIS) are likely to exhibit significant economies of scale in production and operation. Private provision would therefore typically occur under considerable market power. An important policy question is whether the resulting distortions would aggravate or reduce distortions in the transport market itself, notably external effects such as congestion. We consider such questions by presenting an integrated model that captures the interactions between a congested transport market and a monopolistic market for advanced traveller information systems (ATIS). Three market failures operate simultaneously: congestion on the road, a declining average benefit of information when information penetration rises, and monopolistic pricing by the provider of information. Some key results are as follows. Monopoly information pricing appears not to be the most attractive option from a system efficiency viewpoint. A subsidy in the information market can help realise a second-best optimum of road use. Relatively low uncertainty on the road and high information costs limit the monopolist’s profit on the information market, as well as relative system efficiency. While relatively inelastic demand for mobility, counter intuitively, negatively affects the monopolist’s profit, the relative social benefits from private information peak at intermediate demand elasticities.     

Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.     

Impact of ITS measures on public transport: A Case study

Intelligent Transport Systems (ITS) have a wide range of applications. They range from the more traditional signal coordination system to concepts such as smart cars and smart roads. This paper describes transit‐based ITS measures in Singapore. The island‐state has plans to double the current 90 km rail network over the next ten years and has also implemented or committed to implement many ITS initiatives that impact upon the public transport systems. The aim of these investments is to achieve a high transit modal share using a comprehensive transit network. ITS measures that can promote this aim include: automatic vehicle location systems for buses and taxis, integrated transit fare systems using contactless smart cards, rail information systems, multi‐modal travel guides on Internet and electronic road pricing. The potential impacts of these measures are delay reduction, more comfort, productivity gain and better network accessibility. ITS measures do not necessarily add physical capacity to a public transport system but are excellent supporting measures to encourage the modal shift to transit, particularly if a quality transit system is already in place.     

Comparing smart scheme effects for congested highways

A high level objective for many international governments and local operators is that highways should be managed in a way that is sustainable in terms of a Low Carbon Energy future. Recent initiatives such as the Strategic Transport Technology Plan and the policy and legal framework promoted by the European Commissions’ Intelligent Transport System (ITS) Directive and ITS Action Plan may assist with this objective. However, many levels of complexity are inherent within the (ITS) schemes that are now part of highway management, due to the linkage of various technological components to complex systems and services. Maintaining efficient, sustainable co-operative performance is therefore a major task, with inconsistencies between product suppliers, network managers and operators. As a result, it is of considerable interest to the highway operators and high level policy makers to be able to assess the performance of individual ITS schemes and furthermore, to be able to compare performance between ITS schemes. In this paper, an illustration is provided of a methodology that can be used to assess the performance of ITS schemes according to a set of sustainability criteria. A case study is introduced which compares the performance of anticipated Active Traffic Management (ATM) schemes for what the road network operator (Highways England) perceive to be the four most congested highways in England (in terms of annual average daily traffic flows). Appropriate action can then be taken to improve the energy and sustainable management of Information Communication Technology (ICT) and transport systems for the benefit of a smarter, sustainable and efficient future.     

An integrated solution for lane level irregular driving detection on highways

Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5 m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles.     

Environmentally friendly cost efficient and effective sea transport outsourcing strategy: The case of Statoil

Statoil is a completely integrated oil and gas Norwegian company which established in 1972. The company is one of the most technology-intensive world’s leading energy producers and covers more than 35 countries across the globe. This study proposes how Statoil coordinate with environmentally friendly, cost efficient and effective sea transport supplier. The study used the theories of supply chain management such as model of industrial purchasing process, model of strategic triangle and Kraljic purchasing portfolio model. The result of the study identified that the emission of environmentally harmful gases from sea transport are dependent on the size and the speed of the ships, and the type of fuel they consume. The best solution for Statoil to get environmentally friendly sea transport service is using suppliers that are certified by the preferred standard of ITTC 7.5-04-01-01.2 or ISO 15016:2002. The requirement of environmentally friendly sea transport supplier by Statoil maximizes the suppliers’ risk. Therefore, making partnership with the supplier is the effective and giving the contract with a cost reimbursable agreement of fuel price adjustment (escalation clause) is the cost efficient coordination of Statoil.     

Intelligent transport systems solutions in transitional countries: The case of Korea

Transport policy in Korea is driven by the need to cope with ever-worsening traffic conditions. The development of an intelligent transport systems (ITS) Master Plan for Korea is of particular interest and demonstrates the heavy reliance placed on ITS measures to facilitate both the management of congested (but expanding) highway networks and to encourage a shift to greater use of public transport. The paper highlights the major achievements of ITS in Korea by assessing the evidence provided by the widespread implementation of ITS technologies (such as the creation of 'showcase' demonstrations). Some observations from the implementation of ITS in (so-called) transitional economies are presented.     

Intelligent transportation systems: an impact analysis for Michigan

Are Intelligent Transportation Systems (ITS) affecting transportation industry only? ITS are currently perceived as a contribution to transportation industry only; however, by quantitatively analyzing the economic impact of ITS on the state of Michigan, this work attempts to answer the posed question. The quantitative economic analysis is carried out through the well‐established Leontief's Input–Output (I‐O) model. This model is employed to establish ITS effects on each industry by detailing RIMS II I‐O tables for Michigan constructed from the national I‐O tables. Major savings by ITS identified as reduced time delays and fuel cost savings are quantitatively simulated thereby generating an overall cost reduction factor which is incorporated in Michigan I‐O tables to modify their characteristics. ITS impact on each industry in I‐O tables is achieved by maximizing effects on certain selected industries. Impact multipliers that are customary macro‐economic measures for I‐O analysis are then calculated for all the aggregated industries. Multipliers comparison for the three cases namely before ITS implementation, conventional improvement methods, and after ITS implementation is evaluated. These values suggest greater economic benefits that may be achieved by statewide implementation of the ITS. Copyright © 2010 John Wiley & Sons, Ltd.     

Integrated smartcard solutions: do people want one card for all their services?

Smartcard technologies offer the potential for bringing transport and citizen services onto a single smartcard platform – referred to henceforth as an integrated ‘Transport-Citizen’ (TranCit) card. One question never fully addressed thus far is whether the general public see the integration of services onto a single card as desirable. Based upon research undertaken through the Yorcard project trials, this paper presents the analysis of a series of three focus groups and responses to a public questionnaire which investigated the general desire for multi-application, integrated smartcards. Our findings suggest there is potential for an integrated TranCit card, facilitating easier access to services and travel options across boundaries, even at the international level. A number of operational, technical and data security issues exist which need to be resolved before there would be a greater acceptance of an integrated TranCit card, from both the traveller and the service provider perspective.     

Joint leisure travel optimization with user-generated data via perceived utility maximization

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.     

Study on the framework of hybrid collision warning system using loop detectors and vehicle information

Safety warning systems generally operate based on information from sensors attached to individual vehicles. Various types of data used for collision risk calculation can be categorized into two types, microscopic or macroscopic, depending on how the sensors collect the information of traffic state. Most collision warning systems use only either of these types of data, but they all have limitations imposed by the data, such as requirement of high installation cost and high market penetration rate of devices. In order to overcome these limits, we propose a collision warning system that utilizes the integrated information of macroscopic data and microscopic data, from loop detectors and smartphones respectively. The proposed system is evaluated by simulating a real vehicle trip based on the NGSIM data. We compare the results against collision warning systems based on macroscopic data from infrastructure and microscopic data from Vehicle-to-Vehicle information. The analysis of three systems shows two findings that (a) ICWS (Infrastructure-based Collision Warning System) is inadequate for immediate collision warning system and (b) VCWS (V2V communication based Collision Warning System) and HCWS (Hybrid Collision Warning System) produce collision warning at very similar timing, even with different behavior of individual drivers. Advantages of HCWS are that it can be directly applied to existing system with small additional cost, because data of loop detector are already available to be used in Korea and smartphones are widely spread. Also, the computation power distributed to each individual smartphone greatly increases the efficiency of the system by distributing the computation resources and load.     

Orchestration of driving simulator scenarios based on dynamic actor preparation and automated action planning

In driving simulation, a scenario includes definitions of the road environment, the traffic situation, simulated vehicles’ interactions with the participant’s vehicle and measurements that need to be collected. The scenarios need to be designed in such a way that the research questions to be studied can be answered, which commonly imply exposing the participant for a couple of predefined specific situations that has to be both realistic and repeatable. This article presents an integrated algorithm based on Dynamic Actor Preparation and Automated Action Planning to control autonomous simulated vehicles in the simulation in order to generate predefined situations. This algorithm is thus able to plan driving actions for autonomous vehicles based on specific tasks with relevant contextual information as well as handling longitudinal transportation of simulated vehicles based on the contextual information in an automated manner. The conducted experiment shows that the algorithm is able to guarantee repeatability under autonomous traffic flow. The presented algorithm can benefit not only the driving simulation community, but also relevant areas, such as autonomous vehicle and in-vehicle device design by providing them with an algorithm for target pursue and driving task accomplishment, which can be used to design a human-vehicle cooperation system in the coming era of autonomous driving.     

Behavioral monitoring of public transport users through a mobile communication system

There are various activities now taking place in ITS research and development in Japan. Advanced information and communication technologies have been applied to improve public transport systems, as well as automated highway systems. In the first part of this paper, we show three examples of public transport systems recently developed in ITS environment. These transport systems are operated in local cities and towns in Japan: the travel information system for tram users in Hiroshima, the demand responsive bus system in Nakamura and the co‐operative use of electric vehicle in Ebina. In the second part of the paper, we explain how we have monitored individual passenger on public transport using cellular phones for location positioning. Location positioning technology for mobile object is essential for the operation and management of ITS supported public transport systems. Furthermore, such accurate and detailed positioning data can be utilized for travel behaviour analysis in demand modeling. The mobile instrument and monitoring systems shown in this paper can be combined with any of the case studies of ITS application to public transport systems.     

Activity-based disaggregate travel demand model system with activity schedules

We present an integrated activity-based discrete choice model system of an individual’s activity and travel schedule, for forecasting urban passenger travel demand. A prototype demonstrates the system concept using a 1991 Boston travel survey and transportation system level of service data. The model system represents a person’s choice of activities and associated travel as an activity pattern overarching a set of tours. A tour is defined as the travel from home to one or more activity locations and back home again. The activity pattern consists of important decisions that provide overall structure for the day’s activities and travel. In the prototype the activity pattern includes (a) the primary – most important – activity of the day, with one alternative being to remain at home for all the day’s activities; (b) the type of tour for the primary activity, including the number, purpose and sequence of activity stops; and (c) the number and purpose of secondary – additional – tours. Tour models include the choice of time of day, destination and mode of travel, and are conditioned by the choice of activity pattern. The choice of activity pattern is influenced by the expected maximum utility derived from the available tour alternatives.