Travel Time Of Users Research Articles

PUBLIC TRANSPORTATION THROUGH THE CLUSTERING ANALYSIS OF BUS STOPS. CASE STUDY MANIZALES, COLOMBIA

The objective of this research is to identify the main characteristics and geographical distribution of bus stops, associated with collective public transport systems, through data analysis techniques that can identify clustering patterns based on various characteristics of the stops, such as geographical location, passenger flow, service demand, land use, etc. number of routes available, slope of the road, signage, among other relevant factors. The main result of this study is that t he current layout of the stops is distributed according to the main characteristics of their environment, allowing us to conclude that the grouping of transport stops facilitates the interpretation of the location and operability of the stops, thus allowi ng to improve decision-making in the implementation of stops, guaranteeing an adequate operation of the service without compromising coverage and travel time of users.

ЕФЕКТИВНІСТЬ ПЕРЕСУВАНЬ ПАСАЖИРІВ В ІНДИВІДУАЛЬНИХ АВТОМОБІЛЯХ ТА В АВТОБУСАХ, ЩО НА ВИДІЛЕНИХ СМУГАХ

The effectiveness of dedicated lanes for public transport (buses) is determined based on the analysis of publi-cations on the peculiarities of vehicle traffic on the road networks with these lanes. The effectiveness is measured by the amount of reduction in the time it takes passengers to reach their destinations. However, if we are talking about dedicat-ed lanes by reducing their number for other vehicles (cars and trucks), we need to consider the increased travel time for road users in these vehicles. Relevant observations were made in the morning and evening rush hours during the three busiest working days of the week (Monday, Wednesday, Friday), having recorded indicators that can be used to assess traffic flows on sections of the urban road network without and with dedicated lanes (number of buses in dedicated lanes, their speed, travel time; the same for all road users without dedicated lanes). The sections were chosen so that the second one was a continuation of the first one without gaps between them. The result of the study was the establishment of the time and cost efficiency of passenger transportation in buses on a dedicated lane and the same inefficiency of passenger transportation in cars moving on the residual reduced number of lanes. The time costs by the latter were in average 52.61 times less that of passengers traveling in dedicated bus lanes. It is estimated that this increase costs UAH 0.117 per car passenger, compared to UAH 0.096 per bus passenger. In further studies with expanded geography on the implemen-tation of dedicated bus lanes, designers need to make compromise decisions, taking into account all traffic participants, not just buses. Keywords: section of urban road network, lane, dedicated lane, traffic flow, volume, speed, bus, car, duration, effectiveness.

Integrated crew organization and work zone scheduling for network-wide daily road pavement rehabilitation

This study develops a new integer-programming model to address the network-wide daily road pavement rehabilitation scheduling problem. In the model, the crew organization and work zone schedule are jointly optimized daily, with the objective of minimizing both the operational cost and user travel time. A day-to-day traffic dynamics model is applied to capture the non-equilibrium traffic evolution against network supply variation over the planning horizon, which leads to a simulation-based optimization problem. To solve this challenging problem, a two-stage hybrid heuristic solution method is proposed. In the first stage, hybrid tabu search (TS) meta-heuristics are comparatively developed to identify a group of active crew work routes without time slacks. The obtained crew routes are then fed to the second stage for work zone scheduling via a discrete compass search algorithm. Some important findings are obtained from numerical experiments. First, crew routing (or crew organization) is the dominant decision in the studied problem, and a desirable work zone schedule encourages a crew to execute the assigned tasks continually. The findings can be used to develop simplified and efficient solution algorithms. Second, the hybrid TS meta-heuristics developed for crew routing exhibit superior performance compared to other solution methods. Finally, a well-defined model for the current problem should consider both user travel time and operation cost. Our model enables decision-makers to make an effective trade-off between these two objectives. An effective measure is suggested to evaluate the cost-effectiveness of budget investment decisions when budgets are limited.

Cultural tourism attraction recommendation model based on optimized weighted association rule algorithm

To address the challenge of users selecting rich tourism resources, this study proposes a model for cultural tourism attraction recommendation using an optimized weighted association rule algorithm. This model includes time and season weight for tourist attraction recommendations. This model proposes improvement methods to address some inherent issues in traditional tourism recommendation models. Firstly, it constructed a recommendation model for cultural tourism attractions, and then optimized the weighted association rule algorithm by incorporating dynamic time weights. It takes into account the user's intended time in the recommendation outcome. Moreover, it incorporated seasonal weights to optimize the weighted rule algorithm for factors such as user travel time and the attractions' peak season during the recommendation process. The experiment indicates that the F1 value of the improved algorithm model proposed in this study reaches 0.952, the accuracy reaches 0.985, the area under the curve reaches 0.955, the Recall value reaches 0.812, and the fitting degree reaches 0.971. The results suggest that the proposed cultural tourism attraction recommendation model, based on an optimized weighted association algorithm, performs well in recommending tourist destinations. This model can have a positive impact on the development of the tourism industry.

Near-Optimal Scheduling for Crowdsourced Transit System With Skip-Stop Tactic

Efficient bus scheduling is a crucial component for the improvement of public transit services. Without systematic optimization and efficient shift arrangement, the bus scheduling system may suffer from poor vehicle loading rate or crowd onboard, resulting in wasted energy and passenger dissatisfaction. In this paper, we consider a crowdsourced bus service system (on a fixed route) that receives user requests as input and computes the scheduling of buses with flexible departure time and skip-stop to minimize the travel time of users. We first show that the general problem of computing the optimal scheduling is NP-hard. On the other hand, for the case when skip-stop is not adopted, we propose the Optimized Departure Time (ODT) algorithm that computes optimal scheduling. Our algorithm is built on an innovative reduction of the problem to some variants of the k-clustering problem and an efficient application of dynamic programming. On top of ODT, we further improve the effectiveness of the solution by utilizing the power of skip-stop tactic, named ODTS. Our experimental results demonstrate that ODT and ODTS dramatically outperform existing algorithms for the bus scheduling problem in terms of effectiveness and efficiency. Moreover, the solutions given by ODTS are very close to the optimum.

An electric vehicle charging load prediction model for different functional areas based on multithreaded acceleration

This paper proposes an electric vehicle (EV) charging load prediction model for different functional areas based on multithreaded technology. This model comprehensively incorporates the preference characteristics of EV users in charging behavior and mode, as well as accounting for diverse travel purposes. It is worth emphasizing that a comprehensive analysis of the spatiotemporal travel characteristics exhibited by users during different seasons and on working days versus rest days is provided. In terms of temporal scale, refined probability models are employed to accurately fit the user's travel time variables. In terms of the spatial scale, the initial position and spatial transfer probability of users are analyzed. Subsequently, leveraging graph theory, a regional transportation network model is established. On this basis, a novel improved time optimal path decision-making algorithm is proposed by holistically considering factors such as road grade, saturation, traffic conditions, one-way/two-way streets, and delay at signalized intersections. Additionally, the impact of temperature on the upper limit capacity of batteries is analyzed, as well as driving/air conditioning energy consumption models are established. It is noteworthy that a two-stage charging power variation model is introduced, enhancing the precision of charging power calculation. Finally, the effectiveness of the method is validated through case analysis. The results demonstrate a significant disparity in load demand among various functional areas, and the spatiotemporal distribution of load demand is significantly influenced by seasons and working days versus rest days. Furthermore, when employing 4 threads, the proposed method achieves a speedup ratio exceeding 2.5 compared to conventional serial methods.

An optimization model of on-demand mobility services with spatial heterogeneity in travel demand

We propose an optimization model that predicts the required fleet size of on-demand mobility (ODM) services at various service levels for a range of demand patterns. Both customer waiting time and spatial heterogeneity in demand are considered when formulating the flow conservation equation for the system’s steady state. The demand–supply imbalance problem is solved through vehicle relocation. Vehicle relocation is reformulated into a simple linear programming problem, and the objective function is set to minimize the number of relocation vehicles. Furthermore, indicators were defined to more effectively explain the effects of spatial heterogeneity of travel demand on the required fleet size. The analysis results show that even among regions with the same average demand rate, the required fleet size may vary depending on the spatial demand pattern of origins and destinations. The higher the ratio of long-distance travel and the greater the need for vehicle relocation, the larger the fleet size required. Furthermore, the user travel time reduction effect is maximized at the minimum fleet size and then steadily decreases as fleet size increases. Cost minimization requires more vehicles than fleet size minimization. Cost minimization may be an advantageous goal for operation of an ODM service as a driverless vehicle service. The simultaneous consideration of both fleet size minimization and cost minimization provides a required fleet size range. Although the proposed model is simple and includes approximations, it helps provide insight into ODM service design and investment decision-making. Thus, the proposed model can be used by governments or agencies to design policy or operationally optimized services. Moreover, the model can serve as the basis for the practical implementation of planned services.

Factors Influencing Driving Time in Public Transport – A Multiple Regression Analysis

Deviations in driving time (DT), or significant variations, occur frequently on urban public transport (PT) lines, except in subsystems with separate routes. DT variability is the main reason for disturbances in operation, leading to unstable and unreliable transport service. Moreover, it also causes variability in total user travel time, which is one of the main parameters of transport service quality. Identifying and quantifying factors that influence PT vehicle DT characteristics is significant for designing advanced prediction and passenger information systems and prioritising investments to reduce bus travel time and improve the scheduling process, and thus the level of transport service quality. An analysis of the elements of the route and other static elements of the line that influence DT was carried out in this paper. A model for determining and quantifying influential factors and methodologies for collecting all necessary data was created. The multiple regression model, developed as a result of the conducted multivariate statistical analysis using the specialised SPSS software, was applied to the selected representative set of lines in a real urban PT system. The created regression model explains between 18.2% and 97.4% of the variance of average, minimum and maximum DT and its deviation in the peak and off-peak periods.

Public Transport Prioritization and Descriptive Criteria-Based Urban Sections Classification on Arterial Streets

The present paper is aimed at improving minimization methods in traffic flows, particularly reducing the costs of civil transportation through sections of the transport network by giving priority to public transport in densely developed areas. In cities with a radial and radial–circular planning scheme of the road network, where arterial traffic flows converge in the central part, the challenge of street congestion with traffic often arises. As a result, delays of all types of vehicles increase, which causes excessive travel time for users of private and public transport. In this regard, it is proposed to divide the sections of the transport network into eight types based on their geometric parameters and traffic conditions. This differentiation of sections improves the existing methods for determining the spatial delay of traffic flows on sections of the transport network with different parameters. It was achieved by considering the duration of vehicles passing signalized intersections and pedestrian crosswalks and the sections of streets between them, while simultaneously recording the duration of public transport movement, as well as the time they spend at stopping points, using GPS receivers. The results of onsite monitoring and further computations revealed that there are particular urban sections with specific, different distances between adjacent stop lines that are critical for public transport operation. Furthermore, based on the delay criterion, there were three different passage modes proposed to improve the efficiency of the traffic.

Planning of fast charging stations with consideration of EV user, distribution network and station operation

Despite the benefits of electric vehicles (EV) as a sustainable alternative to fossil fuel-powered vehicles, the lack of suitable fast charging stations (FCS) continues to be a barrier to widespread EV adoption. Different factors affecting EV user, distribution network and CS operation must be taken into account when selecting CS locations and capacity. The purpose of this study is to design a FCS location and capacity planning framework that considers EV user travel time and waiting time, distribution network power loss, and station utilization aspects. Besides, a prior utilization rate-based queuing algorithm is proposed to determine the CS capacity, which simultaneously reduce user waiting time and affirms adequate station utilization. Then binary atom search optimization algorithm is employed to solve the FCS location and capacity problem. The proposed model is tested on an integrated transportation network and distribution network and the numerical results are provided and analysed from the perspectives of the distribution network, EV user, and CS operation.

A before-after impact assessment of COVID-19 outbreak on bike-sharing ridership in Washington, DC

ABSTRACT The focus of this research is to assess the impact of COVID-19 outbreak in 2020 on the Capital Bikeshare (CaBi) ridership in Washington, D.C. area compared to 2019. Correlation analysis was conducted and mixed-effects negative binomial (MENB) models were developed to assess CaBi ridership by the type of CaBi user, as the available bike-sharing trip data followed a time-series cross-section panel data structure and the variables (season, stay-at-home order and lift, and weekday) have time-specific effects. The correlation coefficients between CaBi variables, COVID-19 variables, and characteristics of D.C.’s population profile and their statistical significance are sensitive to the year (2019 or 2020), the season of the year (Winter, Spring, Summer, or Fall), and the type of CaBi user (member or casual user). The number of stations between pickups and drop-offs, and the dummy variables representing the before and after stay-at-home order and lift were found significant in the MENB models. There was no significant difference in travel time of CaBi casual users and members during the outbreak. Riding for leisure or commuting has been potentially limited with the stay-at-home order. The findings have practical implications and help bike-sharing operators to increase their resilience during unexpected situations such as the pandemic.

Predictive Proactive Caching in VANETs for Social Networking

Social media traffic constitutes the highest percentage of Internet traffic. Such traffic is largely facilitated by mobile devices, which imposes a huge traffic load on backhaul links in 5G networks, and can in turn affect the quality of service. This traffic load can be alleviated by using vehicular networks as a traffic offloading platform. In particular, vehicles can act as a resourceful asset for edge caching, thus enabling data acquisition from nearby caching nodes rather than the remote backhaul servers. In this paper, we propose the Predictive Proactive Caching Framework (PPCF), which exploits the daily driving routine and predictable behavior of users to pre-cache the data at parked vehicles for the requesters to proactively acquire as they pass by. PPCF is composed of a cache placement module and a prediction module. The former aims at maximizing cache hits by assigning replicas to caching spots that yield maximum certainty in their spatiotemporal availability for requesters. To estimate such availability, the prediction module aims at making accurate travel time predictions by proposing the use of a Long Short-Term Memory (LSTM) network trained using particle swarm optimization (PSO-LSTM). The predicted average travel time is then used to estimate a personalized travel time for users by considering their different driving behaviors. Extensive simulations demonstrate the ability of the proposed framework to achieve significant improvements in its targeted objectives compared to other prominent caching and prediction schemes in vehicular networks.

Its Standardization for BRT

The Bus Rapid Transit (BRT) is an efficient transport system for cities that need to quickly expand its network of public transport, with minimal investment compared to other systems such as the Light Train, and providing a quality service to users. The BRT uses dedicated corridors to reduce user travel time, but these runners is within the existing road network, therefore it is necessary to implement automation systems that can coordinate BRT operations with other modes transport of city. This automation system, known as ITS, has features that perform this management, how to act at the traffic lights along the corridor in order to prioritize the passage of BRT also has other features in other areas that allow proper operation to BRT. This article brings to discussion the functionality of a BRT in order to define a map of required functionality for users, operators, managers, equipment providers, application providers and other interested parties can use as a guide for the execution of its activities.

A road pricing model involving social costs and infrastructure financing policies

This research develops a non-linear continuous optimisation model to estimate tolls for multi-class and multi-period traffic considering integral social costs such as congestion externalities, pavement damage, environmental emissions, operational travel costs, and user travel time cost, in addition to maintenance and construction of road infrastructure costs. The approach uses social welfare principles, formulating a function to calculate the social welfare of a multi-class flow that moves in multiple periods based on a stochastic discrete choice model and considering infrastructure financing constraints. A case study was conducted applying the model to a road in the Colombian Caribbean region and estimated the value of tolls using the particle swarm optimisation heuristic. Results showed that infrastructure policies considering partial financing resulted in higher social welfare values. Scenarios requiring toll revenue to be higher than infrastructure cost can shrink demand and cause distributive issues.

Detour-Impact Index Method and Traffic Gathering Algorithm for Assessing Alternative Paths of Disrupted Roads

Infrastructure plays a key role in society. Recent collapses of bridges have underlined their importance for road functionality, causing disruptions to commuters and emergency vehicles. Major issues arise on rural roads, where the lack of redundancy leads to the isolation of entire communities. Actual approaches to assess the resilience of countryside roads rely on the availability of specific datasets, limiting their practical application; this issue is typically related to traffic data. This research aims to propose innovative algorithms to assess the road network’s vulnerability in rural areas, including a novel traffic data collection process and its calibration. The aggregate metric is called Detour-Impact Index (DII) and compares user costs before and after a disruptive event. The method uses traditional network-impact metrics in combination with a new algorithm that allows us to gather quantitative traffic data starting from qualitative information. User travel time showed good agreement between the proposed procedure and traditional web-based methods. Furthermore, the paper provides user delay costs functions accounting for traffic composition, trip purposes, vehicle operative costs, nonlinear volume–capacity relation, and average daily traffic. A significant aspect is the adaptability of this framework, as it is designed to be coupled with existing approaches. The method is demonstrated on a case study in Tuscany (Italy).

Design of Zonal Express Bus Services to Reduce User Travel Time and Transfers Considering Demand Diversion

Zonal express is an operational strategy for express transit service that only stops in the initial and final zones of its route while skipping stops in-between. Zonal express provides a service with less travel and transfer time by aggregating trip demands with similar origins, destinations, and departure times. In this study, new zonal express routes are designed to reduce the travel and transfer times of transit users efficiently. This study develops a methodology that considers demand diversion. To meet the purpose, the following are conducted: (1) estimation of zonal express demand; (2) clustering of demand with similar origins, destinations, and departure times; (3) selection of candidate clusters; (4) optimization of stops and routes of zonal express; and (5) determining zonal express routes in which the benefit from reduced time and transfers outweighs the cost. The methodology is applied to an actual transportation network in Seoul, South Korea, using smartcard, car navigation, and spatial information data. The result shows that zonal express can reduce users’ travel time and the number of transfers effectively. This study proposes that it is desirable to introduce the zonal express sequentially from the route with the highest net benefit under a budget constraint. This study also presents the results when fares change to provide implications for pricing strategies of the zonal express. Future studies can consider incorporating various objective functions or utilizing vehicles in different time slots.

Incorporating Travel Time Reliability in Equitable Congestion Pricing Schemes for Heterogeneous Users and Bimodal Networks

Congestion pricing is proposed as an effective travel demand management strategy to circumvent the problem of congestion and generate revenue to finance developmental projects. There are several studies focusing on optimal pricing strategies to minimize the congestion level or maximize the revenue of the system. However, with regard to equity issues, benefiting only users with higher value of time is claimed to be the main factor that prevents implementation of such policies in practice. While many studies aimed to tackle the equity issues by certain welfare analyses, most of these studies fail to fully consider realistic features of users’ behavior and the uncertainty in link travel times. Given the variability of travel time in real-world networks and the impacts of pricing policies on path travel time distributions, it is important to consider the users’ reliability valuations, in addition to their travel time valuations. Thus, the goal in this study is to find an equitable pricing scheme that minimizes the total travel time of auto users in a general bimodal network considering heterogeneous users with different values of time and reliability. A particle swarm optimization algorithm is proposed to find self-funded and Pareto-improving optimal toll values. A reliability-based user equilibrium algorithm is embedded into this optimization algorithm to assign travelers to the equilibrated paths for different user classes given toll values. The proposed approach is successfully applied to a modified Sioux Falls network to explore impacts of subsidization, congestion level, and considering travel time reliability on the pricing strategy and its effectiveness.

Modeling and optimization of dedicated bus lanes space allocation in large networks with dynamic congestion

Dedicated bus lanes provide a low cost and easily implementable strategy to improve transit service by minimizing congestion-related delays. Identifying the best spatial distribution of bus-only lanes in order to maximize traffic performance of an urban network while balancing the trade-off between bus priority and regular traffic disturbance is a challenging task. This paper studies the problem of optimal dedicated bus lane allocation and proposes a modeling framework based on a link-level dynamic traffic modeling paradigm, which is compatible with the dynamic characteristics of congestion propagation that can be correlated with bus lane relative positions. The problem is formulated as a non-linear combinatorial optimization problem with binary variables. An algorithmic scheme based on a problem-specific heuristic and Large Neighborhood Search metaheuristic, potentially combined with a network decomposition technique and a performance-based learning process for increased efficiency, is proposed for deriving good quality solutions for large-scale network instances. Numerical application results for a real city center demonstrate the efficiency of the proposed framework in finding effective bus lane network configurations; when compared to the initial network state they exhibit the potential of bus lanes to improve travel time for car and bus users.

Analysis of Trans-Mebidang Bus Rapid Transit (BRT) Policy Implementation in North Sumatra

Trans Mebidang is a Bus Rapid Transit (BRT) system in North Sumatra Province which operates in Medan City, Binjai City, and Deli Serdang Regency. According to the Research and Development Center for Land and Railways at the Ministry of Transportation of the Republic of Indonesia in 2014 the traffic density level in Medan City reached a V / C ratio of 0.769, this has an impact on inconvenience and increases travel time for road users. This study aims to determine the extent of the implementation of the governor's regulations and to determine the supporting and inhibiting factors for the implementation of the Trans Mebidang Bus Rapid Transit (BRT) policy in breaking down congestion in Medan City. In this study, researchers used descriptive methods with observation data collection techniques, interviews and literature studies. The data analysis technique used in this research is analysis using transcripts of the results of interviews, data reduction, analysis, data interpretation and triangulation.The results of the study, researchers found a phenomenon in the field that the implementation of policies in the Regulation of the Governor of North Sumatra Number 31 of 2014 concerning Road-Based Mass Public Transportation Services in the Urban Areas of Medan, Binjai and Deli Serdang. Based on the research focus set in accordance with Edward III's theory, the researcher can conclude that the supporting and inhibiting factors are influenced by aspects of communication, resources, disposition and bureaucratic structure, this can be seen in the summary of the research results that have been presented by the researcher in writing this thesis.

Battery Electric Bus Network: Efficient Design and Cost Comparison of Different Powertrains

Recent advances in the electromobility of bus fleets improve transit service sustainability but require the development of vehicle recharging facilities. The paper defines a methodology to design an efficient transit network operated by battery electric buses in cities with grid-shaped road network, based on continuous approximations. An analytical model defines the optimal network configuration that minimizes the agency cost, the monetization of emissions and the travel time of transit users. The analytical model allows the comparison of total cost, emissions and bus performance to other fuel powertrains. The methodology is tested in Guadalajara (Mexico) to propose an alternative bus configuration, outperforming the current bus service and reducing the agency cost and environmental impact. The analytical model justifies the network rationalization in fewer routes to reduce the total cost of the system. The deployment of standard battery electric buses with opportunity charging scheme obtains the lowest total cost of the system.