Multimodal Transportation最新文献

Exploring approaches for improving the service quality (SQ) of the urban multimodal transportation hub (MMTH) and alluring new passengers for providing seamless and sustainable mobility are the major concerns and objectives for transport planners and operating agencies in the contemporary world. Considering the fact, the present study endeavours to identify the association of users’ socio-demographics and travel characteristics with different service quality levels developed for a MMTH. The data was collected from 515 users of Anand Vihar MMTH, Delhi. K-means clustering categorized SQ in three levels: low, medium, and high. Association Rules Mining approach was used to generate various interesting rules, which also highlighted the disparity among users’ perceptions on the basis of three different SQ levels. Results demonstrated that the ‘passengers travelling 3 to 5 times per week’, ‘those who were having the availability of driving license’, ‘users with more than 15 minutes of transfer time’, and ‘the female users’ were the least satisfied with the SQ of MMTH. Interestingly, ‘the graduates with high-income travelling for work purpose’ and ‘users who were retired from their jobs’ were highly convinced with SQ. The research outcomes will be advantageous to the transport policy makers while implementing the user-based policies in the MMTH for embellishing the public transport infrastructure and to make the urban cities inclusive, resilient, safe and sustainable.

There are many different ways of practicing coworking, and many different forms of CoWorking Spaces (CWSs). In this paper, we define CWSs in economic and spatial terms, and we provide some explanations on the transport mode used to travel to. Is the transport mode used to get to CWSs the same as that which is usually used for travel for work purposes? Does the spatial location of the CWS (in large city - medium-sized town - rural community) have an impact on the transport mode choice? The key issue is determining whether these new working spaces favor a shift in travel behavior toward practices less centered on the car. We use a survey of coworkers conducted in 2019 in the Auvergne-Rhône-Alpes Region (AURA), from which data is processed using a binomial logit model. The estimation results show that the mode choice for traveling to CWSs does not allow us to identify a characteristic that is fundamentally different from mode choice for work purpose. However, the availability of a parking place in CWSs is identified as a possible public policy level.

Multimodal facilities are being built in a majority of urban areas to accommodate both motorized and non-motorized traffic. The resulting transportation system is susceptible to complex interactions between different modes and users. These complex interactions are hard to quantify and capture on a regular basis. There is a need to rely on a microsimulation platform to replicate such real-world scenarios and forecast future conditions. This study focuses on assessing the effect of heterogeneous traffic conditions involving multiple modes like light rail transit (LRT), walking, bicycling, and motorized traffic on vehicle delay at intersections using Vissim traffic simulation software. A 2.5-mile urban arterial corridor comprising of seven at-grade and two grade-separated signalized intersections along US-29 route in the city of Charlotte, NC, USA was chosen for analysis and modeling. Base simulation model was developed by replicating the traffic scenario for the year 2018 (without LRT and non-motorized traffic). External controllers like VisVAP and Viswalk were used to assign signal priority and model heterogenous traffic conditions. The vehicle operational performance of the corridor improved along the major street after the addition of the LRT. An increase in vehicle delay on the major street was observed with an increase in the non-motorized traffic. Contrarily, the operational performance of the cross-streets at at-grade intersections did not see any significant change due to the addition of non-motorized traffic. The proposed framework assists planners to assess the impact of adding a new transit system like LRT and associated non-motorized traffic on the urban corridor.

The majority of cities in both developed and developing countries are confronted with negative externalities from imbalanced land-use and transportation systems. This is mainly due to fast urbanization process as well as the separated planning of the land use and transportation systems. In most of these countries, new towns and cities are being built in the suburban areas to meet the growing demands. However, there are limited methods in the literature detailing how the balance of land use and the transportation system can be examined, especially those involving new town developments. In this study, a framework is proposed. Within the proposed framework, an integrated land-use transport model is developed (using the Production, Exchange, and Consumption Allocation System (PECAS)) and calibrated to forecast land-use activities, which includes a land-use sub-model for estimating future land use patterns and a multimodal transport sub-model to predict travel demand. Furthermore, a method for evaluating the balance between land use activities and the transportation system is adopted and modified at both the local level – traffic analysis zone (TAZs) and system-level – region connecting the new city and main city. The proposed balance evaluation framework is applied to the Yangtze River New City (YRNC) of the City of Wuhan as a case study. The results from the case study show that the proposed framework can be used to examine the balance of land use and transportation system of new towns and cities at both local and regional levels as a versatile decision-support system (DSS) for sustainable planning and development of new towns and cities.

Logistics outsourcing is a practice commonly used by firms to allow them to access capabilities that they lack internally. Although the main drivers of outsourcing in general are fairly well known, the question of what explains logistics outsourcing decisions within the UK pharmaceutical manufacturing industry, in particular, remains under-researched. Therefore, this study aims to bridge the aforementioned gap in the literature. We surveyed 49 drug manufacturers located in the UK using a web-based questionnaire. The data collected were analysed using logistics regression, exploratory factor analysis, and t-tests. We found that UK drug manufacturers regard improving quality and reliability and reducing logistics costs as the most significant reasons for outsourcing logistics services. We also found a direct positive relationship between the service provider's techno-commercial offerings and delivery performance, and the likelihood of being selected to provide these services. We further explored materials transportation, product delivery, research and development, and clinical trials, which are among the most frequently outsourced logistics activities in the UK pharmaceutical manufacturing industry. The study contributes to the wider literature on logistics outsourcing, and more specifically to that on the UK pharmaceutical manufacturing industry. Findings from this research can also be used to guide outsourcing practitioners’ decisions about the selection of logistics service providers. In addition, the study can help to enhance the service providers' understanding of why firms buy logistics services and which services they are likely to buy.

Urban geometry plays a critical role in determining paths for pedestrian flow in urban areas. To improve the urban planning processes and to enhance quality of life for end-users in urban spaces, a better understanding of the factors influencing pedestrian movement is required by decision-makers within the urban design and planning industry. The aim of this study is to present a novel means to assess pedestrian routing in urban environments. As a unique contribution to knowledge and practice, this study: (a) enhances the body of knowledge by developing a conceptual model to assess and classify pedestrian movement behaviours, utilising machine learning algorithms and location data in conjunction with spatial attributes, and (b) extends previous research by revealing spatial visibility as a driver for pedestrian movement in urban environments. The importance of the findings lies in the perspective of revealing novel insights concerning individual preferences and behaviours of end-users and the utilisation of urban spaces. The approaches developed can be utilised for observations in large-scale contexts, as an addition to traditional methods. Application of the model in a high pedestrian traffic-dense retail urban area in London reveals clear and consistent relationships amongst spatial visibility, individuals’ motivation, and knowledge of the area. Key behaviours established in the study area are grouped into two activity categories: (i) Utilitarian walking (with motivation - expert and novice striders) and (ii) Leisure walking (no motivation - expert and novice strollers). The approach offers an insightful and automated means to understand pedestrian flow in urban contexts and informs wider wayfinding, walkability, and transportation knowledge.

Adverse weather conditions like fog, rainfall, and snowfall affect the driver’s visibility, mobility of vehicle, and road capacity. Accurate prediction of the macroscopic traffic stream variables such as speed and flow is essential for traffic operation and management in an Intelligent Transportation System (ITS). The accurate prediction of these variables is challenging because of the traffic stream’s non-linear and complex characteristics. Deep learning models are proven to be more accurate for predicting traffic stream variables than shallow learning models because it extracts hidden abstract representation using layerwise architecture.

The impact of weather conditions on traffic is dependent on various hidden features. The rainfall effect on traffic is not directly proportional to the distance between the weather station and the road because of terrain feature constraints. The prolonged rainfall weakens the drainage system, affects soil absorption capability, which causes waterlogging. Therefore, to capture the spatial and prolonged impact of weather conditions, we proposed a soft spatial and temporal threshold mechanism. To fill out the missing weather data spatial interpolation techniques are used.

The traffic condition on a target road depends on the surrounding area’s traffic and weather conditions and relies on its own traffic characteristics. We designed the hybrid deep learning models, CNN-LSTM and LSTM-LSTM. The former model in the hybrid model extracts the spatiotemporal features and the latter model uses these features as memory. The latter model predicts the traffic stream variables depending upon the passed features and temporal input.

We perform multiple experiments to validate the deep learning model’s performance. The experiments show that a deep learning model trained with traffic and rainfall data gives better prediction accuracy than the model trained without rainfall data. The performance of the LSTM-LSTM model is better than other models in extracting long-term dependency between the traffic and weather data.

An uninterrupted growth of vehicles on road has been a major concern in the urban areas of developing countries, leading to congestion and delay in travel time. Public transport provides accessibility and addresses the adverse effects of using private vehicles to some extent but fails to attract private vehicle users. Park-and-Ride (P&R) is one such facility that not only reduces the number of vehicles on road but also attracts private vehicle users towards using public transport and to do so, the P&R should also be supported by the services that are important for users. This paper aims to determine the importance of different service factors to encourage choice riders towards using P&R as a reliable and sustainable mode of travel compared to drive along. Three analysis methods (GRA, RIDIT, and TOPSIS) were used to identify the influencing factors and the outcomes were further compared to determine the variation (if any). The results show factor of cleanliness as most important to choice riders, followed by the safety at the P&R. Choice riders preferred to have a better quality of service while the cost of using a P&R was of least importance. The procedure explained in this study can assist the decision making authorities to prioritise and offer services that are actually required to attract choice riders.

Free mode choice, termed “synchromodality,” is an extension of intermodal service network design and is still in the early stages of modeling development. European countries have already started moving toward realizing this innovative transportation system. However, advancement in global transport with longer distances is rare and needs more infrastructural preparation and studies to clarify the steps for such a transition. In this paper, an advanced intermodal service network model (AI-SNM) is proposed to support the development of synchromodal transportation systems. This mixed-integer programming (MIP) model finds the optimal path between O/D pairs while considering horizontal integration of variant transport modes in a supply chain network along with resource constraints and time windows. It minimizes the total transportation cost, transshipment cost, and tardiness with a penalty for delays at intermodal terminals and overdue costs at the destination that accounts for the opening and closing times of the terminals. In order to solve the model for large problem instances, an efficient multiobjective genetic algorithm using a novel coding approach is developed. The algorithm is tested on two US-based case studies, showing the capability of the model to provide cost- and time-saving advantages in long-haul freight. The results of this study can be applied to long-distance global transportation with similar geography and scale.

In the business of intermodal passenger transport, fare optimization of intermodal products has significant effects on corporate revenue and passenger travel convenience. This study takes the competitive relationship between high-speed rail (HSR) and airlines as well as carrier connectivity as the starting point, analyzes the advantages and disadvantages of different carriers in the different markets, and researches the optimization of fares. The stochastic user equilibrium model based on elastic demand is used to establish a bi-level programming model for the optimization of fares; the upper and lower models are solved using the particle swarm algorithm and method of successive averages, respectively. The results suggest that airlines are willing to cooperate with the HSR sector and improve the connectivity between aviation and HSR, and a reasonable pricing strategy is more likely to motivate cooperation between aviation and HSR.