Abstract: Efficient transportation system management is possible only through managing travel needs of commuters, using travel demand models. The extend by which a commuter need to travel for accomplishing his/her daily needs is here represented by the total travel time. Total travel time is one of the activity-travel behaviour which is least considered by transportation researchers. Travel demand studies often focus the workers, but give little attention to the students. In a developing country like India, students also contribute a major share in morning and evening peak hour traffic. This study presents the analysis of total travel time for the student community incorporating the socio-demographic features using activity based modelling approach. Preliminary analysis gives details on daily activity-travel pattern, mode choice preferences and other particulars of students in the study area. Statistical models are developed and simulation of choice probabilites is also done for understanding the factors affecting total travel time behaviour, for students in a usual working day.
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The analytical derivative of the TTR is then supplied to the ‘fmincon’ solver in MATLAB to solve the RNDP. The algorithm was tested with a five-link network. The tests were also conducted to investigate the changes of the total travel time distribution as the link capacities varied. The results showed that the link capacity change did not affect only the location of the total travel time distribution but also its dispersion and shape (especially a strong effect of the right-tail of the pdf was found). Additional tests on the influence of the capacity improvement on each link on the TTR were also conducted. The result showed that links which are related to a higher traffic volume and number of paths are likely to have more influence on the TTR. The algorithm was then employed to solve the test problem of RNDP successfully. Further investigation on the sensitivity of the TTR with the budget constraints was also made. The results suggested a gradually decreasing trend of the marginal gain in the TTR improvement as the budget level increased.
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Abstract Multi-objective equilibrium models of traffic assignment state that users of road networks travel on routes that are efficient with respect to several objectives, such as travel time and toll. This concept provides a general framework for mod- elling traffic flow in tolled road networks. We present the concept of time surplus maximisation as a way of handling user preferences. Given a toll, users have a max- imum time they are willing to spend for a trip. Time surplus is this maximum time minus actual travel time. A rational user can be assumed to maximise time surplus, leading to the definition of time surplus maximisation bi-objective user equilibrium. We propose to use such models on the lower level of bi-level models for pricing in road networks under multiple upper level objectives such as minimising total travel time and emissions. In such a model a multi-objective optimisation problem at the upper level is combined with a multi-objective equilibrium problem at the lower level.
In this paper, a novel method is proposed for designing a bike network in urban areas. Based on the number of taxi trips within an urban area, a weighted network is abstracted. In this network, nodes are the origins and destinations of taxi trips and the number of trips among them is abstracted as link weights. Data is extracted from the Taxi smart card system of a real city. Then, Communities i.e. clusters of this network are detected using a modularity maximization method. Each community contains the nodes with highest number of trips within the cluster and lowest number of trips with other clusters. Within each community, the nodes close enough to each other for being traveled by bicycle are detected as key points and some non-dominated bike network connecting these nodes are enumerated using a bi-objective optimization model. The total travel cost (distance or time) on the network and the path length are considered as objectives. The method is applied to Isfahan city in Iran and a total of seven regions with some non-dominated bike networks are proposed.
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Figure 5 shows Alternative 3, with an average LOS of 0.7 which is similar to Alternative 1, however, with different connecting roads such as the lower side of Jalan Terengganu (0.57), Jalan Free School (0.43), Jalan Perak (0.81) and Jalan Datuk Keramat (0.87). The difference with Alternative 1 is that this route choice has a longer distance, which is double that of Alternative 1. Though Alternative 3 has a total travel distance of 4.8 km, longer in comparison with the travel distance of 2.7 km from using Alternative 1, road users will experience similar congestion since Jalan Perak and Jalan Air Hitam have the same LOS of 0.81 and both of these route choices will travel through Jalan Datuk Keramat.
Considering a growing demand for passenger and freight transport and in- creasing congestion levels, this study aims to quantify the effects of replacing the DLT maneuver with the RTUT maneuver on the intersection and network per- formance. A case study is conducted for the Abu Saeed-Khalije Fars intersection, which is considered as one of the most congested areas in the Tehran metropol- itan area (Iran). Two simulation models are built using the Synchro Studio and Aimsun software packages to estimate both intersection and network perfor- mance indicators before and after access management. Application of the de- veloped simulation models will assist with estimating the major intersection and network performance indicators and quantifying potential advantages from re- placing the DLT maneuver with the RTUT maneuver. The remainder of the manuscript is organized in the following manner. The next section presents a review of the literature on access management with focus on implementation of the RTUT maneuver. The third section describes in details the DLT and RTUT maneuvers and provides estimations of the total travel time and the total delay for both maneuvers. The fourth section describes the study area, data collection, and research methodology adopted. The fifth section presents the simulation experiments that were conducted in this study, while the last section summarizes findings and provides future research extensions.
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In signal timing design, GA has been used to optimise cycle time, green time, offsets, and stage sequences (e.g. Foy et al., 1992; Park et al., 2000; Park and Yun, 2005). These applications, however, are limited to problems in which no account is taken of re-routing in the optimisation, andwith the possible exception of Park and Yun who consider a twelve intersection networkare limited to small networks with few signalised intersections. In the network design context, Lee (1998), Taale and Van Zuylen (2003) and Ceylan and Bell (2004) used GA in optimising signal timings, while anticipatingre-routing impacts. For stage length and cycle time optimisation (without considering offsets) to minimize total travel time, Lee (1998) presented a comparison of GA and simulated annealing with iterative and local search algorithms and showed the different algorithms perform better for different network supply and demand scenarios. Van Zuylen and Taale (2003) reported promising results from applying a GA to optimise green times within a NDP context, albeit on small artificial networks. In their approach to the NDP, Ceylan and Bell (2004) used the inverse of a system performance index, defined as the weighed sum of delays and stops for all traffic streams in the network, to optimise a common cycle time, green times and offsets. Flows were constrained to a Stochastic User Equilibrium solution (Daganzo and Sheffi, 1977). Using a small network, they showed that a bi-level framework with GA gives more efficient results than an iterative algorithm in terms of system-wide travel costs. Although with a different application in mind, GA has also been proposed for addressing a number of problems with a similar bi-level structure, such as the analyses of Kim et al (2001) and Stathopoulos and Tsekeris (2004) on the equilibrium-based OD matrix estimation problem; and those of Yin (2000) and Sumalee (2004) on bi-level NDPs.
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Abstract— Traditionally, minimization of the total travel time spent by the passengers in the system is the most preferred objective while planning transit systems. To satisfy this objective, forecasted travel demands between zones and characteristics of the available fleet are taken into account, and at the end of the planning process, optimum frequencies of each transit lines are identified. Unfortunately, this planning approach is not sufficient as the negative effects of the global warming are growing each day. Fossil fuels are the primary energy sources for transport systems and accordingly, the emission of greenhouse gases especially carbon dioxide is accredited to this industry. Hence, it is impossible to ignore environmental requirements in the transit planning phase. In this study, we develop a bi-level and bi-objective optimization model to identify the optimum line frequencies. One of the objectives is to minimize the mean travel time of the passengers. The other objective is to minimize the total emission. A genetic algorithm is developed to solve this mathematical programming problem. A large instance related to Istanbul transit system involving 39 zones and 463 bus lines is solved with this solution method and results are elaborated.
The most important benefit of public transportation is that it reduces the need and desire for private vehicle ownership to some extent and thus can massively reduce the amount of motorized travel. Successful public transport systems that compete with private modes, such as the car and the motorcycle, could retain customers from all social classes (not just the poor) and be used for a wide range of urban trips at all times of the day (not just for trips to work during peak hours). For this to be achieved there is a need for a well-planned public transport system which is affordable and is attractive to all groups of people (Bhandari, Advani, Parida, & Gangopadhyay, 2014).
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Yes, we reset Travel Vouchers to be reduced only. Once the voucher is audited and a minor change is needed, we will contact Client Services have them reset to reduce the vouchers, make the correction in Travel Manager, stamp the voucher 'CFS Adjustment' and email the preparer of the change. Please let us know if this is sufficient notification.
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positive at 0.0106 with uncertainty highlighting a positive im- pact, as high as 0.0438 for the intervention and a negative ef- fect, a higher decrement for the intervention group, at -0.0226; therefore, the effectiveness of the intervention is inconclusive. The cost-effectiveness plane for the base-case analysis (Fig. 1) illustrates the high variability in effect difference with equal proportions of cost-effect pairs present predominantly in the North-East and North-West quadrants. The probability that the NPLI was cost-effective at £20,000 was 45% and at £50,000 was 53%. The economic analysis highlighted high variation in cost- effectiveness estimates based on the assumptions adopted in the scenarios assessed; in particular, the use of the EQ-5D-5L utility score versus the EQ-5D VAS score was one of the main drivers of uncertainty in the results. The assumptions adopted for the base-case analysis and the series of scenario analyses are outlined in Supplementary Table A5. The base-case analysis was not considered cost effective due to inconclusive signifi- cance in the incremental effect contributing to wide CIs, i.e., the uncertainty ranges from positive to negative (Tab. V and Fig. 1). The mean incremental cost associated with the NLPI ranges from £173 to £346 across the series of analyses undertaken. Drivers of variation in cost include the intervention training and delivery costs as well as nurse travel costs, all of which would re- duce with greater efficiency in delivery. ICERs ranged from £40 per QAS (95% CI: £26, £67) to £32,604 per QAS (95% -£16,976,
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Scholars in the tourism industry are continuously looking for new knowledge related to travel behavior, motivations, and the preferences of the main tourist segments. Demographic, socioeconomic, and cultural variables can all influence the demand for travel. Despite numerous studies on most of the demographic and socioeconomic variables, scholars have paid very little attention to religiosity with regard to travel decision-making. Specifically, no researchers have investigated the role of Islamic religiosity in predicting Muslims’ destination choice decisions. The purpose of this dissertation is to examine the role of attitudes, subjective norms, travel motivations, Islamic religiosity, self- efficacy, travel constraints, constraints negotiation strategies, and past behavior on Muslim students’ intentions to travel to a gaming destination. To account for much of the variation in the proposed model, the researcher includes these eight variables. The researcher attempts to explain the relationships between these constructs, as well as their effect on travel behavior. In doing so, the researcher initially hypothesized that Islamic religiosity and travel motivation, apart from influencing travel intention, directly influence Muslims’ attitudes toward gaming destinations. Furthermore, the researcher hypothesized that Islamic religiosity moderates the relationship between Muslims’ attitudes and their travel intentions.
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A completed baseline survey was returned by 3516 (16%) respondents, of whom 1885 (54%) returned a completed follow-up survey one year later. Of these, 232 had missing physical activity or travel data and 25 had extreme differences (>600 min/week) in active travel be- tween baseline and follow-up. Excluding these cases left 1628 respondents. The likelihood of being included in follow-up analysis was associated with being older (56.1% of baseline respondents aged over 65 were included, compared with 30.1% of 18-34-year-olds), owning a home (52.7%, versus 26.3% of those who rented privately) and having access to a car (48.4%, ver- sus 31.5% of those with no car; see Additional file 1). Comparisons with local and national data suggested that on average, respondents were older and more likely to have completed higher education, to own their home, to have access to a car, to be of a normal weight and to be economically active, and less likely to have children liv- ing at home, than the general population (see Additional file 2).
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Dr Aw is Medical Director of the International Travel Clinic at Ultimate Health Medical Centre in Richmond Hill, Ont. Dr Boraston is Medical Director of Vancouver Coastal Health Travel Clinic in British Columbia. Dr Botten is Medical Director at Timbuktu Travel Medicine in Halifax, NS. Dr Cherniwchan is an active staff physician in the Department of Family Medicine at the Abbotsford Regional Hospital and Cancer Centre in British Columbia. Dr Fazal is Lecturer in the Department of Paediatrics at the University of Toronto in Ontario and is Clinic Head at the Markham Travel Clinic in Ontario. Dr Kelton is Assistant Professor in the Department of Family and Community Medicine at the University of Toronto and Medical Director of the Complete Traveler’s Clinic in Toronto. Dr Libman is Director of the Division of Infectious Diseases and Centre for Tropical Diseases at the McGill University Health Centre and is Associate Professor of Medicine at McGill University in Montreal, Que. Dr Saldanha is Medical Director at the Peel Travel Clinic in Mississauga, Ont. Dr Scappatura is Medical Director at the Travel Medicine Centre in Toronto. Mr Stowe is a pharmacist at and the owner of The Prescription Shop at Carleton University in Ottawa, Ont. contributors
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We quantified the spatial effect of various types of dis- turbance on two temporal scales: immediate displace- ment (short-term) and distance travelled during an entire high tide period (longer-term). To identify disturbance events, we observed groups of oystercatchers (among which were birds equipped with GPS trackers) on the Dutch Wadden Sea island of Vlieland, which is exposed to regular military air force training and some recrea- tion. We linked disturbances observed in the field to GPS-tracked movements in order to investigate to what extent high tide travel distance is determined by immedi- ate spatial responses to disturbance, and how this effect compares to the effect of natural sources of environmen- tal variation. Finally, by combining the observed distur- bance frequencies and GPS-tracked travel distances with estimates of flight energetics from the literature, we estimated the impact of disturbance on the daily energy budget of oystercatchers in our study area.
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The Soviet travel and tourism project, as I suggested above, attempted to resolve the dilemma between insider-outsider knowledge by exhorting and training its citizens to become part of the knowledge-producing effort. The Leningrad Ethnographic Museum served at once as a research center and tourist attraction, a space in which official narratives about state-building could be translated for its citizens, but where importantly the visitors themselves were enlisted in writing the narrative. Through “ethnographic evenings” and the museum’s response books, visitors became participants in the construction of ethnographic knowledge. This form of “virtual tourism,” as Francine Hirsch labels it, combined with the popular journal Na sushe i na more (sponsored by the Society for Proletarian Tourism) and with actual tourist experiences to build within the “new Soviet person” that which John Urry has labeled “aesthetic cosmopolitanism.” Real and simulated mobility, Urry writes, gives the cosmopolitan the sense that one has a right to travel anywhere, a curiosity about other peoples and cultures, a willingness to appreciate difference, an ability to locate one’s own society and culture in terms of broader historical and geographical knowledge, and the semiotic skill to be able to interpret tourist signs. 32 (Think museum exhibits, Volga scenery, or Moscow skyscrapers). Travel was good (and not a commodity to be consumed on demand)
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The PoA has appeal as an indicator for policy makers. Bounds on the PoA conveniently set out the maximum possible congestion improvement attainable by rerouting flows. However, not only are the theoretical bounds rarely witnessed in models of real urban networks, but the PoA depends on both the magnitude and configuration of OD demands. Since the OD matrix changes within the day and from day-to-day in a real network, which OD matrix should be used to compute a representative value for the PoA? Moreover, how can we ensure efforts to reroute traffic in order to attain SO flows are focussed on being effective when the OD travel demands offer the greatest gains?
From the secondary sector breakdown we can see that the biggest issue for NHS-related travel is not with NHS business mileage, but with patient travel to and from NHS premises (figure 5 below). Emissions related to electricity and gas use, which account for 2.3 and 1.7 Mt respectively, are comparable to emissions from travel (figure 6). However, a key finding of the study is the emissions related to procurement (figure 7). There are some sectors which are often perceived as important sectors, notably waste and food. However, these have a relatively low carbon impact for the NHS. Our model assigns the carbon embedded in products to the product itself and not to waste. It is emissions embedded in pharmaceuticals and medical instruments that make a significant stamp on the NHS footprint.
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Favouring the crossing of Emergency Vehicles (EVs) through intersections in urban cities is very critical for people lives. There have been several efforts toward developing Traffic Signal Control Systems (TSCS) dedicated to control efficiently the traffic flow, but few are the efforts toward developing Traffic Signal Priority Systems (TSPS) dedicated to favour the crossing of EVs (such as ambulances, firefighters, police cars, etc.). Multi-Agent Systems were considered to develop several distributed TSCS, while very few works have developed distributed TSPS. Such systems lack on dealing with the EVs crossing issues while maintaining a fluid state of the traffic. In the literature, the Longest Queue First – Maximal Weight Matching (LQF-MWM) is proved to guarantee a stable TSCS. Recently, the LQF-MWM technique is increasingly used to benchmarck and assess adaptive TSCS. Moreover, the preemption is one of the most effective techniques used to prioritise the crossing of EVs. This paper is the first to rely on LQF-MWM assumptions, preemption technique, and Multi-Agent Systems to design a distributed TSPS. The suggested system has two main purposes, which are the guidance of EVs and the control of traffic signals. Nine agents are implemented to govern a network of nine intersections, where each agent uses the Multi Agent System based Preemptive Longest Queue First – Maximal Weight Matching. We have referred to VISSIM traffic simulation software for benchmarking and analysis. To assess the suggested system, we have developed a distributed and preemptive version of VISSIM Optimized Stage- Based Fixed-Time algorithm. Python is considered to develop the suggested systems, and Spade platform is considered as agents ’ platform. Several Key Performance Indicators are considered to assess the performance of all controllers including delay time, travel time, vehicles queue occupancy, number of stops, distance traversed, and speed. Experimental results show a competitive performance of the developed system to maintain a fluid traffic and guide efficiency EVs.
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The concept of efficiency in education is widely understood in terms of external and internal efficiency. Internal efficiency refers to the extent to which the educational expenditures are properly utilized for immediate objectives like producing graduates which also includes the aspects such as dropouts, wastage and stagnation, under-utilization of building, library and laboratory. Internal efficiency of an institution depends on the library facilities, materials, and equipment to provide a stimulating learning environment. Internal efficiency includes good teaching in learners’ language, the system, well managed institutions, relevant curriculum, performance appraisal, and well equipped learning centers. Dropout rate is defined as the proportion of children cease to remain in the schooling system. It is seen that planners of education have always expressed concern about educational wastage caused by repeaters and dropouts. Such wastage is one of the difficulties in fulfilling the social demand in formal education. It is required that the retention rate should be improved by reducing educational wastage, while maintaining the quality of the system at reasonable input cost. Educational wastage is an economic term defined as the “total” number of students’ years spent by the repeaters and dropouts. Repeater is a student who in a given school year remains in the same grade as in the previous year; while, dropouts are those who leave the school before the end of the final year or somewhere during an educational cycle in which they are enrolled. Repeaters stay in the school for longer time than normal duration there by reducing the intake
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