Temat: transportation demand management - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Transportation demand management as a tool of transport policy
Autorzy:: Barcik, R.
Bylinko, L.
Powiązania:: https://bibliotekanauki.pl/articles/374908.pdf
Data publikacji:: 2018
Wydawca:: Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:: transport policy
transportation demand management
suburbanization
polityka transportowa
zarządzanie popytem na transport
suburbanizacja
Opis:: This article presents the concept of transportation demand management (TDM) in the EU transport policy context. Authors present the source of transport intensity problems and also show good practices that effectively reduce the transport demand. To identify the major transportation demand problems that cities are faced with, primary and secondary research was carried out. Primary research shows the social awareness of mobility management. Secondary research consists of a thorough review of the existing literature on transport problems faced by cities. From this view, it is clear that transport in relation to the length of the roads in Poland is one of the highest in Europe. This paper also indicates that the current urban policies are often the main causes of suburbanization.
Źródło:: Transport Problems; 2018, 13, 2; 121-131
1896-0596
2300-861X
Pojawia się w:: Transport Problems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Attaining a good primal solution to the uncapacitated transportation problem
Autorzy:: Juman, Z. A. M. S. Silmi
Nawarathne, N. G. S. A.
Hisam, M. S. M.
Powiązania:: https://bibliotekanauki.pl/articles/2141215.pdf
Data publikacji:: 2022
Wydawca:: Fundacja Centrum Badań Socjologicznych
Tematy:: logistics and supply chain management
uncapacitated transportation problem
supply and demand requirements
primal solution
minimal total cost solution
Opis:: Transportation of products from sources to destinations with minimal total cost plays an important role in logistics and supply chain management. The Uncapacitated Transportation Problem (UTP) is a special case of network flow optimization problem. The prime objective of this UTP is to minimize the total cost of transporting products from origins to destinations subject to the respective supply and demand requirements. The UTP consists of special network structure. Due to the special structure of this problem, the transportation algorithm is preferred to solve it. The transportation algorithm consists of two major steps: 1) Finding an Initial Feasible Solution (IFS) to TP and 2) Examining the optimality of this IFS. A better IFS generates a lesser number of iterations to obtain a Minimal Total Cost Solution (MTCS). Recently, Juman and Nawarathne (2019)’s Method was introduced to find an IFS to UTP. In this paper, the Juman and Nawarathne (2019)’s Method is improved to get a better IFS to a UTP. A comparative study on a set of benchmark instances illustrates that the new improved method provides better primal solutions compared to the Juman and Nawarathne (2019)’s Method. The proposed method is found to yield the minimal total cost solutions to all the benchmark instances.
Źródło:: Journal of Sustainable Development of Transport and Logistics; 2022, 7, 1; 51-61
2520-2979
Pojawia się w:: Journal of Sustainable Development of Transport and Logistics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Modelling of Travel Behaviour of Students Using Artificial Intelligence
Autorzy:: Alex, Anu P.
Manju, V. S.
Isaac, Kuncheria P.
Powiązania:: https://bibliotekanauki.pl/articles/224051.pdf
Data publikacji:: 2019
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: travel demand models
travel behaviour
transportation planners
travel management
econometric model
podróże
zachowania podróżujących
planowanie podróży
zarządzanie podróżą
model ekonometryczny
Opis:: Travel demand models are required by transportation planners to predict the travel behaviour of people with different socio-economic characteristics. Travel behaviour of students act as an essential component of travel demand modelling. This behaviour is reflected in the educational activity travel pattern, the timing, sequence and mode of travel of students. Roads in the vicinity of schools are adversely affected during the school opening and closing hours. It enhances the traffic congestion, emission and safety problems around schools. It is necessary to improve the safety of school going children by understanding the present travel behaviour and to develop efficient sustainable traffic management measures to reduce congestion in the vicinity of schools. It is possible only if the travel behaviour of educational activities are studied. This travel behaviour is complex in nature and lot of uncertainty exists. Selection of modelling technique is very important for modelling the complex travel behaviour of students. This leads to the importance of application of artificial intelligence (AI) techniques in this area. AI techniques are highly developed in twenty first century due to the advancements in computer, big data and theoretical understanding. It is proved in the literature that these techniques are suitable for modelling the human behaviour. However, it has not been used in behaviourally oriented activity based modelling. This study is aimed to develop a model system to predict the daily travel behaviour of students using artificial intelligence technique, ANN. These ANN models were then compared with the conventional econometric models developed. It was observed that artificial intelligence models provide better results than econometric models in predicting the activity-travel behaviour of students. These models were further applied to study the variation in activity-travel behaviour, if short term travel-demand management measures like promoting walking for educational activities are implemented. Thus the study established that artificial intelligence can replace the conventional econometric methods for modelling the activity-travel behaviour of students. It can also be used for analysing the impact of short term travel demand management measures.
Źródło:: Archives of Transport; 2019, 51, 3; 7-19
0866-9546
2300-8830
Pojawia się w:: Archives of Transport
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Impact assessment of short-term management measures on travel demand
Autorzy:: D'Cruz, Jinit J.M.
Alex, Anu P.
Manju, V. S.
Peter, Leema
Powiązania:: https://bibliotekanauki.pl/articles/223500.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: public transportation
travel demand management
four stage model
linear regression
modal shift
multinomial logit model
transport publiczny
zarządzanie podróżą
przesunięcie modalne
Opis:: Travel Demand Management (TDM) can be considered as the most viable option to manage the increasing traffic demand by controlling excessive usage of personalized vehicles. TDM provides expanded options to manage existing travel demand by redistributing the demand rather than increasing the supply. To analyze the impact of TDM measures, the existing travel demand of the area should be identified. In order to get quantitative information on the travel demand and the performance of different alternatives or choices of the available transportation system, travel demand model has to be developed. This concept is more useful in developing countries like India, which have limited resources and increasing demands. Transport related issues such as congestion, low service levels and lack of efficient public transportation compels commuters to shift their travel modes to private transport, resulting in unbalanced modal splits. The present study explores the potential to implement travel demand management measures at Kazhakoottam, an IT business hub cum residential area of Thiruvananthapuram city, a medium sized city in India. Travel demand growth at Kazhakoottam is a matter of concern because the traffic is highly concentrated in this area and facility expansion costs are pretty high. A sequential four-stage travel demand model was developed based on a total of 1416 individual household questionnaire responses using the macro simulation software CUBE. Trip generation models were developed using linear regression and mode split was modelled as multinomial logit model in SPSS. The base year traffic flows were estimated and validated with field data. The developed model was then used for improving the road network conditions by suggesting short-term TDM measures. Three TDM scenarios viz; integrating public transit system with feeder mode, carpooling and reducing the distance of bus stops from zone centroids were analysed. The results indicated an increase in public transit ridership and considerable modal shift from private to public/shared transit.
Źródło:: Archives of Transport; 2020, 53, 1; 37-52
0866-9546
2300-8830
Pojawia się w:: Archives of Transport
Dostawca treści:: Biblioteka Nauki

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