Multimodal Transportation最新文献

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.