Search Results (25)

This study develops a high-speed rail demand prediction model based on access probability, which quantifies the likelihood of passengers choosing a departure station among multiple alternatives. Traditional models assign demand to the nearest station or rely on manual calibration, often failing to reflect actual travel behavior and requiring excessive time and resources. To address these limitations, this study integrates survey data, real-world datasets, and machine learning techniques to model station choice behavior more accurately. Key influencing factors, including headway, access time, parking availability, and transit connections, were identified through passenger surveys and incorporated into the model. Machine learning algorithms improved prediction accuracy, with SHAP analysis providing interpretability. The proposed model achieved high accuracy, with an average error rate below 3% for major stations. Scenario analyses confirmed its applicability in network expansions, including GTX openings and the integration of mobility as a service. This model enhances data-driven decision-making for rail operators and offers insights for rail network planning and operations. Future research will focus on validating the model across diverse regions and refining it with updated datasets and external data sources. Full article

With the continuous advancement of Mobility as a Service (MaaS), a hybrid traffic flow comprising MaaS-based and conventional trips has emerged within transportation networks, leading to diverse behaviors among heterogeneous travelers. Given the coexistence of heterogeneous travelers during the promotion of MaaS, this paper investigates two distinct groups: travelers using MaaS subscription services (defined as “subscribed users”) and traditional travelers who rely on personal experience (defined as “decentralized users”). Accordingly, we propose a day-to-day and within-day bi-level dynamic traffic assignment model for heterogeneous travelers under the MaaS framework. By optimizing subscribed users’ travel decisions, this model assists urban planners in predicting the evolution of mixed traffic flows, enabling improved road resource allocation and subscription service mechanisms. For the day-to-day component, the model explicitly incorporates mode-switching behaviors among heterogeneous travelers. In the within-day context, departure time and route choices are considered, along with travel time costs and additional costs arising from early or late arrivals. Consequently, we propose a within-day, time-dependent traffic assignment model specifically tailored for heterogeneous users. For modeling subscribed users’ traffic assignment, we develop a system-optimal (SO) bi-level programming model aiming at minimizing the total travel cost. Furthermore, by integrating an improved Genetic Algorithm with the Method of Successive Averages (MSA), we introduce an enhanced IGA-MSA hybrid algorithm to solve the proposed model. Finally, numerical experiments based on the Nguyen–Dupuis network are conducted to evaluate the performance of the proposed model and algorithm. The results indicate that the network with heterogeneous MaaS users can reach a steady state effectively, significantly reducing overall travel costs. Notably, decentralized users rapidly shift towards becoming subscribed users, highlighting the attractiveness of MaaS platforms in terms of cost reduction and enhanced travel experience. Additionally, the IGA-MSA hybrid algorithm effectively decreases overall travel costs in the early evolution stages and achieves a more balanced temporal distribution of trips across the system, effectively managing congestion during peak periods. Full article

The pre-departure reserved time (PDRV) for high-speed railway (HSR) passengers, which encompasses all the time between passengers leaving their origin and the departure of the HSR train they are going to take, is a crucial factor in planning intercity travel. Understanding how passengers select their PDRV is not only important for developing effective strategies to improve HSR efficiency but also for optimizing the integration between HSR hubs and urban transportation networks. However, analyzing passenger choice behavior regarding PDRV is complex due to numerous influencing factors. Despite this, few studies have explored how HSR passengers make their PDRV choices. This paper, using Nanjingnan Railway Station as a case study, presents a novel investigation into the PDRV choice behavior of HSR passengers. An integrated latent class model (LCM) and ordered probit model (OPM) are applied to identify the factors affecting passengers’ PDRV choices. The sample data are segmented based on individual characteristics using the LCM, and OPM models are then constructed for each segment to analyze PDRV choice behavior. The results reveal that several factors—such as travel purpose, the number of times passengers used HSR at Nanjingnan Station in the previous year, the duration of HSR travel, the number of companions, feeder trip duration, and departure time—significantly impact PDRV choices. The integrated LCM and OPM approach also uncovers choice heterogeneity among different passenger groups. These insights can serve as a valuable reference for forecasting HSR passenger demand and for designing integrated HSR hubs and urban transport systems. Full article

Walking is the final phase of the morning commute, during which commuters are exposed to vehicular emissions. This study proposes a novel analytical model to evaluate how emission exposure affects commuters’ departure time choices and parking behavior. Different from traditional bottleneck models, our model includes a nonlinear term in the generalized cost function to account for emission exposure. The findings reveal that, at user equilibrium, rational commuters seeking to minimize their own generalized costs will park outward, resulting in undesired scenarios in which all walking commuters suffer from emission exposure. However, we show that in a system-optimal scenario, emission exposure can be eliminated if commuters park inward; the schedule delay cost is minimized in such a parking order. To achieve this outcome, we propose a new spatiotemporal parking pricing scheme designed to reduce the overall system cost and incentivize inward parking patterns. Case studies using empirical data show that this pricing approach, independent of specific parking orders, effectively encourages inward parking, thereby minimizing emissions and improving commuter welfare. Hopefully, findings from this research can provide insights to the development of effective roadside parking pricing strategies. Full article

This study formulates the joint decisions of commuters on departure time and parking location choices in a morning commute problem where the commuters travel with autonomous vehicles (AVs) or human-driven vehicles (HVs). Under a mixed traffic environment, we aim to explore the impacts of parking capacity and parking pricing on the equilibrium travel pattern and the system performance. We build a dynamic equilibrium model for the morning commute problem by assuming that the parking slots can be grouped into central and peripheral clusters based on the distance between the parking location and the workplace. We first analyze the parking location preferences of commuters towards the two parking clusters under a mixed traffic environment. We then examine the equilibrium conditions and identify all the equilibrium travel patterns. We further analyze the system performance measured by the total travel cost with respect to the parking prices and the capacity of the central cluster. The optimal parking pricing scheme is also derived to minimize the total travel cost. We conduct numerical analysis to demonstrate the change in the total travel cost against the parking capacity of the central cluster and its parking price. Sensitivity analysis is performed to show the impacts of the network configuration on the total travel cost. Full article

Microsatellites are codominant markers that, due to their high polymorphism, are a common choice for detecting genetic variability in various organisms, including fungi, plants, and animals. However, the process of developing these markers is both costly and time-consuming. As a result, the cross-species amplification has become a more rapid and more affordable alternative in biological studies. The objective of this study was to assess the applicability of 13 SSR markers, originally designed for Carex curvula, in other 14 species belonging to different sections of the genus. All the markers were successfully transferred with a mean of 90.76%, and 100% transferability was reached in two species (C. baldensis and C. rupestris). The lowest transferability was registered in the G165 marker, which did not produce amplification in six species. Together, the microsatellites amplified a total of 183 alleles, ranging from 10 to 19 alleles per locus, with an average of 14.07. The mean number of different alleles ranged from 0.846 to a maximum of 2.077 per locus. No significant departures from the Hardy–Weinberg equilibrium were detected in polymorphic loci. The transferability of the 13 SSR markers proved highly successful in various Carex species, across different clades and sections of the genus. Full article

Populations in conflict contexts often live for extended periods of time in displacement or under occupation. Both have profound consequences for navigating daily wellbeing. Drawing on narrative interviews (n = 8) with participants who lived through the ISIS (Islamic State) occupation of Mosul in 2014–2017, we seek to highlight narratives of wellbeing- and illbeing-emerging from their experiences. Our case study suggests that multiple persistent threats forced a renegotiation of ways to sustain key elements of wellbeing. Our findings suggest that intentionally propagated distrust led to reduced interaction, while insecurity and fear diminished personal freedoms, causing recurring shocks requiring constant adaptation. Decreasing the size of the core social unit helped families manage risks and resources when facing existential threats, while the diversification of interpersonal and communal relations created space for moments of normalcy. Choices made in order to stay safe and sane under such exceptional circumstances include complex relational choices, such as breaking familial ties with loved ones. Our research expands on the positive and negative impacts of relations on wellbeing and deepens our understanding of how wellbeing is navigated in contexts of forced departure—environments from which people often flee to seek refuge elsewhere. Full article

Determining the mode choice for movement in developing cities like Dhaka is beset with multifaceted challenges and intricacies, rendering it an arduous undertaking. Numerous factors contribute to the complexity, thereby impeding the selection of an optimal transportation mode. Bangladesh University of Engineering and Technology (BUET) attracts students from various regions and cultures in Dhaka city. Examining users’ perceptions of preferred mode choice is the primary objective of this study. Transportation performance of buses and institutional buses was considered as most of the off-campus students are highly dependent on these two modes. Structural equation modeling (SEM) was implemented to create two distinct empirical models to investigate the correlations between key factors that impact public transportation mode choice. Models were calibrated using data from 1664 respondents who were formally surveyed about their expectations, experiences, and opinions regarding their usual means of transportation. There were 20 attributes of travel experience including safety, comfort, cost, travel time, waiting time, convenience, reliability, availability, environment friendliness, driver behavior, overtaking tendency, vehicle speed, obeying the law, accident probability, weather, punctuality of arrival and departure, etc. Policy implications have been analyzed in the context of a developing country such as Bangladesh from the perceived ratings on mode choice so that by providing reliable, efficient, and student-friendly transportation options, educational institutions, planners, and transportation authorities can support the success and overall well-being of off-campus students. Full article

Optimizing the train plan for high-speed rail systems should consider both the passengers’ demands and enterprise’s benefits. The choice of the departure time period is the most important factor affecting the passenger demand distribution. In this paper, the optimization problem of a train operation plan based on time period preference is studied for a high-speed rail corridor. First, according to the travel process of the passengers, the extended service network for a high-speed rail system is established. The main factors that influence the passengers’ travel choices are analyzed, and the departure time period preference, stop time and flexible pricing strategy based on the time period preference are put forward. The generalized travel cost function, including the convenience, ticket fare and stop time costs, is constructed, and a two-level programming model is established based on the function. The upper-level planning model is formulated as a mixed 0–1 programming problem that aims at maximizing the revenue of the railway enterprise. It is mainly constrained by passenger travel demand and solved by improved genetic algorithms. The lower-level model is a user equilibrium (UE) model. The Frank–Wolfe algorithm is used to allocate multiple groups of OD (origin and destination) passenger flows to each train so that the generalized travel expenses of all the passengers with the same OD are minimized and equal. Finally, the train operation plan is solved based on the Lan-xi (Lanzhou–Xi’an) high-speed rail data, and the validity of both the model and algorithm is verified. Full article

Travel mode selection is a crucial aspect of traffic distribution and forecasting in a comprehensive transportation system, which has significant implications for resource allocation and optimal management. As commuters are the main part of urban travel, studying the factors that affect their choice of transport mode plays a crucial role in urban traffic management and planning. Based on public transport operation data, a travel chain is created by identifying boarding stations, alighting stations, and transfer behaviors, and includes detailed travel information. The regression and correlation coefficients of departures and arrivals at stations are confirmed to be 0.98 and 0.92 in the presented data, indicating the viability of the recognition method. Then, multiple travel modes are identified based on the origin and destination, and the proportion of mode selection is determined by the actual travel chain. Using maximum likelihood estimation (MLS) and NLOGIT software, the random parameter logit (RPL) mode is used to estimate the relationship between travel mode selection and characteristic variables such as travel time, distance, cost, comfort, walking distance, and waiting time. The results indicate that walking distance, travel distance, and comfort have a greater influence on travel choice, and that walking distance is a random parameter with a normal distribution, reflecting the diversity of commuters. In addition, this paper discusses the influence degree of the change of characteristic variables of a transport mode on the choice between it and other modes. These results can be used as reference for relevant departments to make measures to improve the overall efficiency of the urban transportation system. Full article

This study investigates the morning commute problem in a single corridor with a bottleneck under a fully automated vehicle environment. By extending Vickrey’s bottleneck model, we formulate the joint decisions on departure time and parking choices of morning commuters who make trade-offs among travel cost, the cost associated with parking, and the cost of tradable credits. To alleviate traffic congestion and improve social welfare, we propose a time-varying tradable credit scheme and integrate it with the morning commute problem. We explore the travel patterns and the optimal design of tradable credit schemes for the morning commute problem with homogeneous and heterogeneous commuters, respectively. For the homogeneous case, we derive the conditions on the tradable credit scheme to ensure the existence of equilibrium. The system-wide travel cost decreases with parking density after tradable credits are incorporated. Additionally, the efficiency of the tradable credits scheme can be improved by increasing the rate of credit charge rate. For the heterogeneous case, we propose an initial distribution strategy and combine it with the optimal tradable credit in order to guarantee social equity. The commuters with a low value of time (VOT) should be allocated more credits and the commuters with high VOT should be charged more credits. For both cases at system optimum, we find that the equilibrium price of tradable credits increases with parking density and decreases with the total amount of tradable credits. Full article

In addition to a wide range of socio-economic impacts, traffic congestion during the era of the COVID-19 pandemic has been identified as a critical issue to be addressed. In urban neighborhoods, the timespan of traffic congestion hazard (H_TC) after the curfew lift is subjected to the commuters’ decisions about home-to-shopping center departures. The decision for departing early or late for shopping depends on both the internal (commuter related) and external (shopping center related) factors. The present study developed a practical methodology to assess the H_TC period after the curfew timings. An online questionnaire survey was conducted to appraise the commuters’ perception about departure time and to assess the impact of eight internal (family size, involvement in other activities, nature of job, education level, age, number of vehicles, number of children, and availability of personal driver) and three external (availability of shopping center of choice in near vicinity, distance to shopping center, and size of the city) factors on their decision. With an acceptable 20% response rate, Chi-square and Cramer’s V tests ascertained family size and involvement in other activities as the most significant internal factors and availability of shopping center of choice as the primary external factor. Age, number of children, and size of the city influenced to some extent the commuters’ decisions about early or delayed departure. Large associations were found for most of the factors, except education level and availability of drivers in a household. Fuzzy synthetic evaluation (FSE) first segregated the commuters’ responses over a four level-rating system: no delay (0), short delay (1), moderate delay (3), and long delay (5). Subsequently, the hierarchical bottom-up aggregation effectively determined the period of highest traffic congestion. Logical study findings revealed that most (about 65%) of the commuters depart for shopping within 15 min after the curfew lift, so H_TC in the early part (the first one hour) of the no curfew period needs attention. The traffic regulatory agencies can use the proposed approach with basic socio-demographic data of an urban neighborhood’s residents to identify the H_TC period and implement effective traffic management strategies accordingly. Full article

Despite offering often significant advantages with respect to other flying machines, especially in terms of flight endurance, airships are typically harder to control. Technological solutions borrowed from the realm of shipbuilding, such as bow thrusters, have been largely experimented with to the extent of increasing maneuverability. More recently, also thrust vectoring has appeared as an effective solution to ameliorate maneuverability. However, with an increasing interest for high-altitude airships (HAAs) and autonomous flight and the ensuing need to reduce weight and lifting performance, design simplicity is a desirable goal. Besides saving weight, it would reduce complexity and increase time between overhauls, in turn enabling longer missions. In this perspective, an airship layout based on a set of non-tilting thrusters, optimally placed to be employed for both propulsion and attitude control, appears particularly interesting. If sufficiently effective, such configurations would reduce the need for control surfaces on aerodynamic empennages and the corresponding actuators. Clearly, from an airship design perspective, the adoption of many smaller thrusters instead of a few larger ones allows a potentially significant departure from more classical airship layouts. Where on one side attractive, this solution unlocks a number of design variables—for instance, the number of thrusters, as well as their positioning in the general layout, mutual tilt angles, etc.—to be set according simultaneously to propulsion and attitude control goals. In this paper, we explore the effect of a set of configuration parameters defining three-thrusters and four-thrusters layout, trying to capture their impact on an aggregated measure of control performance. To this aim, at first a stability augmentation system (SAS) is designed so as to stabilize the airship making use of thrusters instead of aerodynamic surfaces. Then a non-linear model of the airship is employed to test the airship in a set of virtual simulation scenarios. The analysis is carried out in a parameterized fashion, changing the values of configuration parameters pertaining to the thrusters layout so as to understand their respective effects. In a later stage, the choice of the optimal design values (i.e., the optimal layout) related to the thrusters is demanded to an optimizer. The paper is concluded by showing the results on a complete numerical test case, drawing conclusions on the relevance of certain design parameters on the considered performance, and commenting the features of an optimal configuration. Full article

Normality testing remains an important issue for researchers, despite many solutions that have been published and in use for a long time. There is a need for testing normality in many areas of research and application, among them in Quality control, or more precisely, in the investigation of Shewhart-type control charts. We modified some of our previous results concerning control charts by using the empirical distribution function, proper choice of quantiles and a zone function that quantifies the discrepancy from a normal distribution. That was our approach in constructing a new normality test that we present in this paper. Our results show that our test is more powerful than any other known normality test, even in the case of alternatives with small departures from normality and for small sample sizes. Additionally, many test statistics are sensitive to outliers when testing normality, but that is not the case with our test statistic. We provide a detailed distribution of the test statistic for the presented test and comparable power analysis with highly illustrative graphics. The discussion covers both the cases for known and for estimated parameters. Full article

Familiarity with a route is influenced by levels of dynamic and static knowledge about the route and the route network such as type of roads, infrastructure, traffic conditions, purpose of travel, weather, departure time, etc. To better understand and develop route choice models that can incorporate more meaningful representations of route familiarity, OBDII devices were installed in the vehicles of 32 drivers, 65 years and older, for a period of three months. Personalized web-based trip diaries were used to provide older drivers with post-trip feedback reports about their risky driving behaviors, and collect feedback about their route familiarity, preferences, and reasons for choosing the route driven vs. an alternate low-risk route. Feedback responses were analyzed and mapped onto an abstraction hierarchy framework, which showed that among older drivers, route familiarity depends not only on higher abstraction levels such as trip goals, purpose, and driving strategies, but also on the lower levels of demand on driving skills, and characteristics of road type. Additionally, gender differences were identified at the lower levels of the familiarity abstraction model, especially for driving challenges and the driving environment. Results from the analyses helped highlight the multi-faceted nature of route familiarity, which can be used to build the necessary levels of granularity for modelling and interpretation of spatial and contextual route choice recommendation systems for specific population groups such as older drivers. Full article