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The number of vehicles on the road (worldwide) is constantly increasing, causing traffic jams and congestion especially in city traffic. Anticipatory vehicle routing techniques have thus far been applied to fairly small networked traffic scenarios and uniform traffic. We note here a number of limitations of these techniques and present a routing st...
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... et al., 2011) limit the changes and only accept the changes which result in a significant improvement and hence control the highly dynamic nature of the problem in this way. Here we take part of the road map of Reading, United Kingdom as the city map given in Figure 1. Routing may be classified into centralized approaches ( Kuwahara et al., 2010) and decentralized approaches (Pavlis and Papageorgiou, 1999). ...
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Citations
... Many factors affect the reliability of bus travel time, such as Inadequate green time, pedestrian crossings, and overdevelopment. Although many travelers face traffic delay caused by congestion and (Kala and Warwick 2015;Zhu et al. 2021). ...
Public transportation provides basic mobility services to various types of facility actions including those involving education, employment, medical care, and recreation. The general goal of this study was to develop a model with respect to travel time. Public bus transport which connects two cities, A/Hitam- B/Pahat), was selected to conduct this study. Factors such as departure delays, route length, delays at marked traffic light intersections, delays at bus stops, and bus speeds were collected using observation methods. SPSS is a methodological tool used for statistical analysis. The results of the analysis showed a strong positive correlation of ‘0.942’ between travel time and route length, a moderate positive correlation of ‘0.697’ between travel time and delay at marked traffic light intersections, a weak correlation of ‘0.272’ between travel and departure time. delay with a significance level of 0.05. The determination coefficient showed a great regression explanatory power with R2 = 0.954. and (P <0.001). SPSS is also used to examine the data and generate models.
... As observed by Loo et al., (2007), cities, as places of intense concentrations of humankind and activities, are depicted as fast or slow living ecologies (Loo et al., 2007). Other authors offer depictions that include the elevated cost of habitats (Lefebvre, 2003;Minton, 2017;Mumford, 1938), congestion (Ferguson, 2019;Kala and Warwick, 2015;Tyme,1978), air and boundary pollution (Barlow, 2014;Fenger et al., 1998), traffic congestion (Appleyard and Lintell, 1972;Tranter, 1996), and causal mental unwellness (Corburn, 2019;Devlin, 2018;Evans, 2003) which differ strikingly with the archetypal portrayal of idealised retirement or cultural contentment (Galbraith, 1992;Vaillant and DiRago., 2006) of city inhabitants in late life. ...
Abstract
This study sought to determine the factors that impact on the health and well-being of residents aged fifty five years and older, living in an inner-city community in Dublin, the capital city of Ireland.
Data was extracted from ten in-depth, semi-structured interviews, which were coded using the Quirkos coding programme. All participants lived in The Liberties community and were aged fifty five years and older,, representing 16% of the population, compared to a national average of 24%. The gentrification and ‘studentification’ of available land and accommodation, following a legacy within the area of urbanised industrialisation, was found to have had a disruptive impact on the study’s cohort. While poor health emerged as a significant and detrimental factor, ease of access to good healthcare was identified as an advantage of city centre living.
Other positive aspects that came to the fore were the strong sense of community spirit nurtured by both formal and informal networks and the willingness of participants to engage with educational resources and social activities. Financial instability, poor standards of accommodation, a concern for physical public security and a dearth of both green spaces and recreational facilities were highlighted as issues requiring input from health, housing and planning authorities.
The unwillingness of the participants to reach out beyond their family networks to state-funded services to access care packages for their own requirements or the needs of elderly relatives, combined with a reluctance to plan toward their own future health and welfare needs, also suggested a need for the relevant authorities to devise new strategies, which utilise existing collectivised networks, for community gain.
... With known travel time on each edge, fastest route is determined using A-star graph traversal algorithm, commonly used in routing simulations [43]. However, velocities in the system change dynamically with agents movement, see equation (2), deprecating initially chosen paths. ...
The invention and implementation of smart connected cars will change the way how the transportation networks in cities around the world operate. This technological shift will not happen instantaneously—for many years, both human-driven and smart connected vehicles will coexist. In this paper, using a multiagent simulation framework, we model a complex urban transportation system that involves heterogeneous participants. Vehicles are assigned into two groups: the first one consists of smart cars and the second one involves regular ones. Vehicles in the former group are capable of rerouting in response to changes in the observed traffic while regular ones rely on historical information only. The goal of the paper is to analyze the effect of changing smart cars penetration on system characteristics, in particular, the total travelling time. The smart car routing algorithm proposed in this paper reduced travelling time up to 30%. Analysis has shown that the behaviour of the system and optimal configuration of underlying algorithms change dynamically with smart vehicles penetration level.
... Accelerating urbanization is increasing the global urban population and number of motor vehicles, leading to conflict between increasing traffic demand and limited traffic capacity (Kala and Warwick 2015;Kong et al. 2016;Su et al. 2017). Effectively managing rising traffic congestion requires comprehensive access to real-time data and better understanding of the mechanisms driving congestion in urban road networks (Huang et al. 2012;Liu et al. 2015;Ye et al. 2018). ...
Clarifying the spatiotemporal characteristics of urban traffic congestion can help to manage and alleviate such congestion more effectively. In this study, we used real-time traffic data provided by the Gaode LBS open platform to model and analyse urban traffic congestion in Xi’an, China, focusing on the spatiotemporal characteristics of single roads, different road grades, and the whole road network at different moments and time periods. Weekday real-time traffic data were collected from 23 April to 11 May, 2018, and the congestion index for different types of roads within Xi’an’s Third Ring Road was calculated. The results allowed us to thoroughly analyse the spatiotemporal characteristics of traffic congestion in Xi'an and suggest methods for congestion control and improvement, including enhanced traffic control during special periods and more balanced urban development. Our results show the feasibility of this evaluation approach, which can provide timely decision-making support for urban traffic congestion management.
... If the deliberative planning is done without considering the other robots, it is evident that many robots will be scheduled in the same areas at the same time, increasing congestion in the same areas. An inspiration is taken from the problem of routing in transportation systems [7,8], wherein it is fundamentally known that if all the vehicles take the shortest or the fastest route, the congestion at the popular roads will be excessively large. Congestions cause none of the vehicles to move for prolonged times, thus significantly increasing the time of travel. ...
... Further of all the regions that host the largest distance, the middle point of the region hosting the largest gap is taken. The general concept is shown in Fig. 4. The angular speed ω reactive i is set so as to obtain the direction of travel φ, given by Eq. (8). Equation (8) is again an abuse of notation as the angle differences need to maintain the circular property, which is not shown in the equation. ...
... The continuous dynamics of the system for the state is already described by Eqs. (5)(6)(7)(8)(9). A deadlock is said to have occurred when the robot is not moving for dead units of time, excluding very minor position adjustments. ...
In the future, many teams of robots will navigate in home or office environments, similar to dense crowds operating currently in different scenarios. The paper aims to route a large number of robots so as to avoid build-up of congestions, similar to the problem of route planning of traffic systems. In this paper, first probabilistic roadmap approach is used to get a roadmap for online motion planning of robots. A graph search-based technique is used for motion planning. In the literature, typically the search algorithms consider only the static obstacles during this stage, which results in too many robots being scheduled on popular/shorter routes. The algorithm used here therefore penalizes roadmap edges that lie in regions with large robot densities so as to judiciously route the robots. This planning is done continuously to adapt the path to changing robotic densities. The search returns a deliberative trajectory to act as a guide for the navigation of the robot. A point at a distant of the deliberative path becomes the immediate goal of the reactive system. A ‘centre of area’-based reactive navigation technique is used to reactively avoid robots and other dynamic obstacles. In order to avoid two robots blocking each other and causing a deadlock, a deadlock avoidance scheme is designed that detects deadlocks, makes the robots wait for a random time and then allows them to make a few random steps. Experimental results show efficient navigation of a large number of robots. Further, routing results in effectively managing the robot densities so as to enable an efficient navigation.
... The basic mechanism for a lane change is assumed to be an overtaking based lane change [23]. It is assumed that the traffic operates using a 'keep-left' rule and overtaking takes place on the right. ...
Research on intelligent transportation systems is so far focussed on decreasing the travel time of vehicles and avoiding congestions. However, the importance of reaching on time is different for different vehicles and depends upon the purpose of the journey. In a human-operated queue, it is generally considered courteous to give priority to people running very late. They may be running late to catch a flight or may be in an emergency for a medical check-up. There is a very small discomfort to the other people as long as the number of people in an emergency and running late is low. However, such prioritization is an invaluable help to the people running late. In this paper the same behaviour is modelled, wherein the transportation system is made biased towards the vehicles on an important task and running late. The paper presents the mechanism by which a vehicle may judge its running status, decide whether to ask for cooperation and decide how much of cooperation to ask for. The vehicle lane changes and traffic lights operating policy are made cooperative. Experimental results show that such a cooperation leads to lesser number of important vehicles reaching their destinations late.
Modern cities are becoming significantly affected by traffic congestion. New wireless communications and advanced sensing technologies have led to development of Connected Vehicles (CVs) and Autonomous Vehicles (AVs) (and their combinations CAVs), which will enable driverless cars of the future. In the airspace there is a concept of urban air mobility (UAM) that promises to organize intercity air traffic by utilizing a new generation of Vertical takeoff and landing (VTOL) aircraft. However, such concepts require that some important topics are addressed, e.g.: infrastructural support for the VTOLs, UAM traffic rules, and consequently organization of an integrated traffic management. To address some of these gaps we propose a novel concept, called Urban Surface and Air Mobility (USaAM), which is based on utilization of the Flying Vehicles (FVs) both on urban surface streets and in urban air space. We further propose to regulate use of such FVs by utilizing certain eligible urban corridors for take-off/landing operations and by introducing a limited number of Street Flight Levels to handle vertical separation of potentially conflicting FVs. We implement a unified traffic management of the surface and airborne traffic in an agent-based simulation platform to investigate impact of various FV penetration rates in a couple of traffic demand scenarios. Our preliminary results show that the use of FVs in the USaAM has a significant potential to reduce some common congestion performance measures. Although intuitive and explainable, the results should be further validated both in simulation and with the field data.
It is becoming increasingly likely that in the future most vehicles will be semiautonomous with communication capabilities and will possess diverse speed capabilities. Here, the creation of an integrated traffic simulation system for such settings is described. The behaviours of crossing, overtaking, intelligent traffic lights operating system, dynamic speed limits for speed lanes, lane reservation, road reservation, density regularization or traffic jam avoidance, replanning for blockages and rerouting under slow-traffic conditions are presented. From this the attention is focussed towards the specific topic of routing. The number of vehicles on the road (worldwide) is constantly increasing, causing traffic jams and congestion. The study is specifically conducted with the assumption of a city map that has a large number of nodes and connectivity and in which the vehicles possess highly varying speed capabilities. Frequent short-term planning is preferred as compared to infrequent planning of the complete map.
