Roadmaps to Utopia: Tales of the Smart City

Notions of the Smart City are pervasive in urban development discourses. Various frameworks for the development of smart cities, often conceptualized as roadmaps, make a number of implicit claims about how smart city projects proceed but the legitimacy of those claims is unclear. This paper begins to address this gap in knowledge. We explore the development of a smart transport application, MotionMap, in the context of a £16M smart city programme taking place in Milton Keynes, UK. We examine how the idealized smart city narrative was locally inflected, and discuss the differences between the narrative and the processes and outcomes observed in Milton Keynes. The research shows that the vision of data-driven efficiency outlined in the roadmaps is not universally compelling, and that different approaches to the sensing and optimization of urban flows have potential for empowering or disempowering different actors. Roadmaps tend to emphasize the importance of delivering quick practical results. However, the benefits observed in Milton Keynes did not come from quick technical fixes but from a smart city narrative that reinforced existing city branding, mobilizing a growing network of actors towards the development of a smart region. Further research is needed to investigate this and other smart city developments, the significance of different smart city narratives, and how power relationships are reinforced and constructed through them

Modelling driver distraction effects due to mobile phone use on reaction time

Phone use during driving causes decrease in situation awareness and delays response to the events happening in driving environment which may lead to accidents. Reaction time is one of the most suitable parameters to measure the effect of distraction on event detection performance. Therefore, this paper reports the results of a simulator study which analysed and modelled the effects of mobile phone distraction upon reaction time of the Indian drivers belonging to three different age groups. Two different types of hazardous events: (1) pedestrian crossing event and (2) road, crossing event by parked vehicles were included for measuring drivers' reaction times. Four types of mobile phone distraction tasks: simple conversation, complex conversation, simple texting and complex texting were included in the experiment. Two Weibull AFT (Accelerated Failure Time) models were developed for the reaction times against both the events separately, by taking all the phone use conditions and various other factors (such as age, gender, and phone use habits during driving) as explanatory variables. The developed models showed that in case of pedestrian crossing event, the phone use tasks: simple conversation, complex conversation, simple texting and complex texting caused 40%, 95%, 137% and 204% increment in the reaction times and in case of road crossing event by parked vehicles, the tasks caused 48%, 65%, 121% and 171% increment in reaction times respectively. Thus all the phone use conditions proved to be the most significant factors in degrading the driving performance. (C) 2017 Elsevier Ltd. All rights reserved

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services. © 2009 Elsevier Ltd. All rights reserved

Improving the performance of a topological map-matching algorithm through error detection and correction

Map-matching algorithms integrate data from positioning sensors with a digital map in order, first, to identify the road link on which a vehicle is traveling, and second, to determine the vehicle's location on that link. Due to errors in positioning sensors, digital maps, and the map-matching (MM) process, MM algorithms sometimes fail to identify the correct road segment from the candidate segments. This phenomenon is known as mismatching. Identification of the wrong road link may mislead users and degrade the performance of a location-based intelligent transportation system (ITS) and services. The main objective of this article is to improve a topological map-matching (tMM) algorithm by error detection, correction, and performance re-evaluation. Errors in a tMM algorithm were determined using data comprising 62,887 positioning points collected in three different countries (the United Kingdom, the United States, and India). After map-matching, each mismatched case was examined to identify the primary causes of the mismatches. A number of strategies were developed and applied to reduce the risk of mismatching thus enhancing the tMM algorithm. An independent data set of 5,256 positioning points collected in and around Nottingham, UK, was employed to re-evaluate the performance of the enhanced tMM algorithm. The original tMM algorithm correctly identified the vehicle's position 96.5% of the time; after enhancement this increased to 97.8%. This compares very well with the performance of tMM algorithms reported in the literature. The enhanced tMM algorithm developed in this research is simple, fast, efficient, and easy to implement. Since the accuracy offered by the enhanced algorithm is found to be high, the developed algorithm has potential to be implemented in real-time location-based ITS applications. Copyright © Taylor and Francis Group, LLC

Map-aided integrity monitoring of a land vehicle navigation system

The concept of user-level integrity monitoring has been successfully applied to air transport navigation systems, where the main focus is on the errors associated with the Global Positioning System (GPS)-data-processing chain. Little research effort has been devoted to the study of integrity monitoring for the case of land vehicle navigation systems. The primary difference is that it is also necessary to consider errors associated with a spatial map and a map-matching (MM) process when monitoring the integrity of a land vehicle navigation system. This is because these two components play a vital role in land vehicle navigation. To date, research has focused on either the integrity of raw positioning data obtained from GPS or the integrity of the MM process and digital map errors. In this paper, these sources of error are simultaneously considered. Therefore, the main contribution of this paper is to report the development of a user-level integrity-monitoring system that concurrently takes into account all the potential error sources associated with a navigation system and considers the operational environment to further improve performance. Errors associated with a spatial road map are given special attention. Two knowledge-based fuzzy inference systems were developed to measure the integrity scale. The performance of the integrity method was assessed using field data collected in Nottingham and London, U.K. The results indicate that the integrity method provides valid warnings 98.2% and 99.4% of the time for positioning data in a mixed operational environment in Nottingham and suburban areas of London, respectively