Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions

The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC) framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain. Document type: Articl

Revisiting the past : replicating fifty-year-old flow analysis using contemporary taxi flow data

JR wishes to acknowledge the support of the EPSRC (Grant # EP/I018433/1). JR, UD and MB were supported by the European Commission grant COSMIC: Complexity in Spatial Dynamics (Complexity-NET/FP6 ERANET) during the early stages of this work. MB further wishes to thank the European Research Council for support under 249393-ERC-2009-AdG.Over sixty years ago, geography began its so-called quantitative revolution, where for the first time statistical methods were used to explain the spatial nature of geographic phenomena. Computers made some of this possible, but their limited power did not allow for more than relatively small analytic explorations and consequently many of these earlier ideas are now buried in the mists of time. Here we attempt to replicate one of these early analyses using taxi flow data collected in 1962 and originally used by Goddard (1970 Goddard, J. B. 1970. Functional regions within the city centre: A study by factor analysis of taxi flows in Central London. Transactions of the Institute of British Geographers 49:161–82; then at the London School of Economics) to extract functional regions within London's city center. Our experiment attempts to replicate Goddard's methodology on a modern taxi flow data set, acquired through Global Positioning System tracking. We initially expected that our analysis would be directly comparable with Goddard's, potentially providing insights into temporal change in the spatial structure of the city core. Attempts at replicating the original analysis have proved enormously difficult, however, for several reasons, including the many subjective choices made by the researcher in articulating and using the original method and the specific characteristics of contemporary taxi flow data. We therefore opt to replicate Goddard's approach as closely and as logically as possible and to fill in gaps based on statistically informed choices. We have also run the analysis on two spatial scales—Central London and a wider area—to explore how scales of analyses that were beyond the capacities of Goddard's early computations also help to shape our understanding of the results he obtained.Publisher PDFPeer reviewe

Using mobility data as proxy for measuring urban vitality

In this paper, we propose a computational approach to Jane Jacobs\u27 concept of diversity and vitality, analyzing new forms of spatial data to obtain quantitative measurements of urban qualities frequently employed to evaluate places. We use smart card data collected from public transport to calculate a diversity value for each research unit. Diversity is composed of three dynamic attributes: intensity, variability, and consistency, each measuring different temporal variations of mobility flows. We then apply a regression model to establish the relationship between diversity and vitality, using Twitter data as a proxy for human activity in urban space. Final results (also validated using data sourced from OpenStreetMap) unveil which are the most vibrant areas in London

Understanding road congestion as an emergent property of traffic networks

ABSTRACT Despite a considerable amount of research into the modelling of traffic flow through road networks, a clear understanding of the conditions that cause and exacerbate urban road congestion remains elusive. This paper presents a novel approach to this problem, by identifying congestion as an unintentional emergent property of driver-to-driver interactions. It is described how human interaction is a constant and important intrinsic property of city driving, and how these interactions can lead to explicit phenomena. A framework is presented for the further analysis of congestion at three spatial levels, and the driver behaviours that may be of most significance at each described. Building on this model, the paper presents a case study carried out at the highly congested Blackwall Tunnel in London. In providing evidence for some of the concepts described in this paper, the case study demonstrates how human behaviour causes the emergence of road congestion. The concepts and framework presented in this paper represent a strong starting point for further understanding and eventual modelling of the occurrence and spread of road congestion

Agent-based modeling and the city: A gallery of applications

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities

Exploring the Role of Spatial Cognition in Predicting Urban Traffic Flow through Agent-based Modelling

Urban systems are highly complex and non-linear in nature, defined by the behaviours and interactions of many individuals. Building on a wealth of new data and advanced simulation methods, conventional research into urban systems seeks to embrace this complexity, measuring and modelling cities with increasingly greater detail and reliability. The practice of transportation modelling, despite recent developments, lags behind these advances. This paper addresses the implications resulting from variations in model design, with a focus on the behaviour and cognition of drivers, demonstrating how different models of choice and experience significantly influence the distribution of traffic. It is demonstrated how conventional models of urban traffic have not fully incorporated many of the important findings from the cognitive science domain, instead often describing actions in terms of individual optimisation. We introduce exploratory agent-based modelling that incorporates representations of behaviour from a more cognitively rich perspective. Specifically, through these simulations, we identify how spatial cognition in respect to route selection and the inclusion of heterogeneity in spatial knowledge significantly impact the spatial extent and volume of traffic flow within a real-world setting. These initial results indicate that individual-level models of spatial cognition can potentially play an important role in predicting urban traffic flow, and that greater heed should be paid to these approaches going forward. The findings from this work hold important lessons in the development of models of transport systems and hold potential implications for policy

Prospectus, April 28, 1982

BOARD PLANS FOR COLLEGE EXPANSION; News Digest; Candidates present views; Previewing really is censorship; Second StuGo forum flops on its back; ERA supporters rally together at capital; Don\u27t push your opinions, beliefs on others; Variety of students enroll in art transfer program; P.C. Happenings...: May Day festival to be featured, Music program to be performed, Phi Beta Lambda attends convention, Walks planned through woods, Help yourself to health, Learning to adjust to newborn baby; Parkland serves area through TV classes; Event offers chance to sell food; Origin of Arbor Day hazy; Parkland plans for Arbor Day activity; Soft pretzels make good party snacks; Rathskeller jams at 1st Parkland outdoor concert; Classifieds; Gettin\u27 lucky rocking with Loverboy; \u27Swamp Thing\u27 nothing but entertainment; Talking with the roadies; Tornado Shelter Guide; Mother Nature at her worst...: Watch for tornadoes; Mayor discusses Champaign projectshttps://spark.parkland.edu/prospectus_1982/1020/thumbnail.jp

The impact of spatial orientation changes on driving behavior in healthy aging

Objectives: Global cognitive changes in older age affect driving behavior and road safety, but how spatial orientation differences affect driving behaviors is unknown on a population level, despite clear implications for driving policy and evaluation during aging. The present study aimed to establish how spatial navigation changes affect driving behavior and road safety within a large cohort of older adults. Methods: Eight hundred and four participants (mean age: 71.05) were recruited for a prospective cohort study. Participants self-reported driving behavior followed by spatial orientation (allocentric and egocentric) testing and a broader online cognitive battery (visuomotor speed, processing speed, executive functioning, spatial working memory, episodic memory, visuospatial functioning). Results: Spatial orientation performance significantly predicted driving difficulty and frequency. Experiencing more driving difficulty was associated with worse allocentric spatial orientation, processing speed, and source memory performance. Similarly, avoiding challenging driving situations was associated with worse spatial orientation and episodic memory. Allocentric spatial orientation was the only cognitive domain consistently affecting driving behavior in under 70 and over 70 age groups, a common age threshold for driving evaluation in older age. Discussion: We established for the first time that worse spatial orientation performance predicted increased driving difficulty and avoidance of challenging situations within an older adult cohort. Deficits in spatial orientation emerge as a robust indicator of driving performance in older age, which should be considered in future aging driving assessments, as it has clear relevance for road safety within the aging population

Knowledge and behavior of Nigerian dentists concerning the treatment of children with special needs

BACKGROUND: Children with special needs (CSN) are reported to receive less adequate dental care due to various behavioral problems and barriers created by dental professionals. This study was carried out to determine the knowledge and behaviour of Nigerian dentists concerning the treatment of CSN. METHODS: Questionnaires consisting of open and closed ended questions requesting socio-demographic information, type of practice, undergraduate and postgraduate training, self-rated knowledge and behaviour concerning care of CSN, were hand delivered to 359 dentists in the 3 geographical zones of Nigeria over a period of 8 weeks. Responses were compared across age groups, gender, type of practice and training received. RESULT: Two hundred and eighty questionnaires were returned completed, constituting 79.9% response rate. Most of the respondents were aged 30 – 39 years (44.3%). There were more males (56.1%) and more recent graduates of 10 years and below (78.5%). Over 80% of respondents had treated children with disabilities, those with physical disabilities being most encountered. Only 19.3% of respondents rated their knowledge of management of CSN as adequate, with no significant difference across age groups and gender, but with a significantly higher number of older graduates reporting to have adequate knowledge (p < 0.05). Those who had undergraduate training in care of CSN were 69.5% compared with only 12.8% who had post graduate training. Only 11.8% rated their undergraduate training as adequate. Thirty seven percent of respondents rated the CSN they had treated as very challenging. A higher proportion of older graduates (of more than 10 years post graduation) and those who rated their undergraduate training as inadequate used sedation and general anaesthesia. Seventy one percent of respondents were willing to treat CSN, with no significant difference across age groups, gender and training, but with a significantly higher percentage among those who had rated their knowledge as adequate. Most of those who were unwilling to treat CSN felt their management was tedious and challenging. CONCLUSION: From this study, very few dentists reported to have adequate knowledge of management of CSN, irrespective of age, gender and place of practice. A significant number of those with more experience rated their knowledge as adequate. Although most dentists rated the children's behaviour as challenging, they indicated their willingness to treat them in their practices