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The purpose of this study is to provide real-time route guidance within city to help commuters.

In urban areas to avoid road congestion and to reach the destination on time, intelligent transport system (ITS) utilizes recent advanced technology. To support this, existing route guidance system (RGS) suggests alternative route to commuters. However, ITS requires a system which suggests the alternative route along with the mode of transport such as public, private, taxi services etc. Integrated mode of transport (IMT) implemented in this paper guides the commuters of urban area with the best mode of transport. Inputs to our IMT predictive model are the commuter's choice of (1) minimum travel time (2) minimum cost (3) flexible route and (4) less traffic intensity along with source and destination locations. Based on these user inputs, IMT predictive model suggests optimal mode of transport. In this paper to implement the IMT model, we have considered the transport facility available in Bangalore, a city in India. The city has metro train, bus and taxi services available to the commuters. Implementation is divided into two parts. In the first part, the model checks for the end-to-end connectivity/availability of metro train facility. If metro train connectivity exists, the model concludes this as the best mode of travel. In the second part, for the routes which are not connected by metro train, the optimal mode of transport through road network will be suggested. In the first part, to check the existence of metro train along the routes between source and destination, location-IQ API is used. In the later part, to suggest transport along the road network, Q-learning algorithm of reinforcement learning technique is used.

The findings are the predictive model algorithm to find the best mode of transfer and reinforcement model used in real time route guidance system.

This is a new Idea, not proposed in any research work.

This paper aims to offer a tutorial/introduction to new statistics arising from the theory of optimal transport to empirical researchers in econometrics and machine learning.

Presenting in a tutorial/survey lecture style to help practitioners with the theoretical material.

The tutorial survey of some main statistical tools (arising from optimal transport theory) should help practitioners to understand the theoretical background in order to conduct empirical research meaningfully.

This study is an original presentation useful for new comers to the field.

The purpose of this paper is to propose a stochastic optimisation model for integrating service and maintenance policies in order to solve the queuing problem and the cost of maintenance activities for public transport services, with a particular focus on urban ropeway system.

The authors adopt the following approaches: a discrete-event model that uses a set of interrelated queues for the formulation of the service problem using a cost-based expression; and a maintenance model consisting of preventive and corrective maintenance actions, which considers two different maintenance policies (periodic block-type and age-based).

The work shows that neither periodic block-type maintenance nor an age-based maintenance is necessarily the best maintenance strategy over a long system lifecycle; the optimal strategy must consider both policies.

The maintenance policies are then evaluated for their impact on the service and operation of the transport system. The authors conclude by applying the proposed optimisation model using an example concerning ropeway systems.

This is the first study to simultaneously consider maintenance policy and operational policy in an urban aerial ropeway system, taking up the problem of queuing with particular attention to the unique requirements public transport services.

This purpose of this paper is to present a methodology for optimally planning long‐haul road transport activities through proper aggregation of customer orders in separate full‐truckload or less‐than‐truckload shipments in order to minimize total transportation costs.

The model is applied to a specific Italian multi‐plant firm operating in the plastic film for packaging sector. The method, given the order quantities to be shipped and the location of customers, aggregates shipments in subgroups of compatible orders resorting to a heuristic procedure and successively consolidates them in optimized full truck load and less than truck load shipments resorting to a Genetic Algorithm in order to minimize total shipping costs respecting delivery due dates and proper geographical and truck capacity constraints.

The paper demonstrates that evolutionary computation techniques may be effective in tactical planning of transportation activities. The model shows that substantial savings on overall transportation cost may be achieved adopting the proposed methodology in a real life scenario.

The main limitation of this optimisation methodology is that an heuristic procedure is utilized instead of an enumerative approach in order to at first aggregate shipments in compatible sets before the optimisation algorithm carries out the assignments of customer orders to separate truckloads. Even if this implies that the solution could be sub‐optimal, it has demonstrated a very satisfactory performance and enables the problem to become manageable in real life settings.

The proposed methodology enables to rapidly choose if a customer order should be shipped via a FTL or a LTL transport and performs the aggregation of different orders in separate shipments in order to minimize total transportation costs. As a consequence, the task of logistics managers is greatly simplified and consistently better performances respect manual planning can be obtained.

The described methodology is original in both the kind of approach adopted to solve the problem of optimising orders shipping in long‐haul direct shipping distribution logistics, and in the solution technique adopted which integrates heuristic algorithm and an original formulation of a GA optimisation problem. Moreover, the methodology solves both the truckload assignment problem and the choice of LTL vs FTL shipment thus representing an useful tool for logistics managers.

This paper aims to identify the factors that experts consider important for the effective operation of multimodal transport using the Trans-Siberian Railway (TSR) in the Russian Far East and to also establish operational strategies that enable Korean logistics companies to use TSR efficiently for multimodal transportation.

A total of 65 questionnaires were distributed to predominantly Korean logistics companies/shippers with experience in TSR and 19 replies were received. The majority of the respondents are senior executives with at least 10 years of work experience. To identify the factors and present strategies, this study applied the strengths, weaknesses, threats and opportunities -fuzzy analytic hierarchy process (FAHP) method, which is based on complex multiple-criteria decision-making (MCDM) that facilitates decision-makers in considering an operational strategy by calculating the importance of input factors.

Results from the FAHP indicate that the Strengths is the main factor affecting TSR multimodal transport decisions, followed by the Threats, Opportunities and Weakness factors. In terms of rank, reduction of transportation distance and time are determined as the most crucial factor from the perspective of Korean logistics companies, followed by the northern logistics development plan, the Russian Government's willingness to the development plan, political instability and possible linkages with the Korean ports.

Through a hybrid MCDM analysis of Korean logistics companies, three operational strategies were proposed: foreign direct and small investments in the Russian Far East, Korean logistics companies implementing a cold-chain logistics service in the fishery industry and a government-led “Block Train Integration System” to secure the minimum threshold volume of cargo for small and medium-sized companies.

Due to the growing percentage share of urban dwellers, the physical distribution of products faces altering conditions. This research explores the effects that urbanization has on the performance of a fast-moving consumer goods distribution network. A focus is set on changes in distribution cost, the cost-minimal network design, and greenhouse gas emissions.

The analyses are based on a quantitative distribution network model of an existing manufacturer of consumer goods.

The results indicate that the foreseen population shift will affect the network's economic and environmental performance. Effects are, among others, due to differences in the efficiency of supplying urban and nonurban regions. The combined effects of urbanization and the development of the population size will even more affect the network's performance.

Research dealing with distribution logistics and urbanization primarily focuses on city logistics. In this paper, the object of analysis is the entire distribution system.