Understanding travel mode choice: A new approach for city scale simulation

Understanding travel mode choice behaviour is key to effective management of transport networks, many of which are under increasing strain from rising travel demand. Conventional approaches to simulating mode choice typically make use of behavioural models either derived from stated preference choice experiments or calibrated to observed average mode shares. Whilst these models have played and continue to play a key role in economic, social, and environmental assessments of transport investments, there is growing need to gain a deeper understanding of how people interact with transport services, through exploiting available but fragmented data on passenger movements and transport networks. This thesis contributes to this need through developing a novel approach for urban mode choice prediction and applying it to historical trip records in the Greater London area. The new approach consists of two parts: (i) a data generation framework which combines multiple data-sources to build trip datasets containing the likely mode-alternative options faced by a passenger at the time of travel, and (ii) a modelling framework which makes use of these datasets to fit, optimise, validate, and select mode choice classifiers. This approach is used to compare the relative predictive performance of a complete suite of Machine Learning (ML) classification algorithms, as well as traditional utility-based choice models. Furthermore, a new assisted specification approach, where a fitted ML classifier is used to inform the utility function structure in a utility-based choice model, is then explored. The results identify three key findings. Firstly, the Gradient Boosting Decision Trees (GBDT) model is the highest performing classifier for this task. Secondly, the relative differences in predictive performance between classifiers are far smaller than has been suggested by previous research. In particular, there is a much smaller performance gap identified between Random Utility Models (RUMs) and ML classifiers. Finally, the assisted specification approach is successful in using the structure of a fitted ML classifier to improve the performance of a RUM. The resulting model achieves significantly better performance than all but the GBDT ML classifier, whilst maintaining a robust, interpretable behavioural model.Funding provided by UK Engineering and Physical Sciences Research Council via the Future Infrastructure and Built Environment Centre for Doctoral Training (EP/L016095/1)

Modeling travel demand and crashes at macroscopic and microscopic levels

Accurate travel demand / Annual Average Daily Traffic (AADT) and crash predictions helps planners to plan, propose and prioritize infrastructure projects for future improvements. Existing methods are based on demographic characteristics, socio-economic characteristics, and on-network (includes traffic volume) characteristics. A few methods have considered land use characteristics but along with other predictor variables. A strong correlation exists between land use characteristics and these other predictor variables. None of the past research has attempted to directly evaluate the effect and influence of land use characteristics on travel demand/AADT and crashes at both area and link level. These land use characteristics may be easy to capture and may have better predictive capabilities than other variables. The primary focus of this research is to develop macroscopic and microscopic models to estimate travel demand and crashes with an emphasis on land use characteristics. The proposed methodology involves development of macroscopic (area level) and microscopic (link level) models by incorporating scientific principles, statistical and artificial intelligent techniques. The microscopic models help evaluate the link level performance, whereas the macroscopic models help evaluate the overall performance of an area. The method for developing macroscopic models differs from microscopic models. The areas of land use characteristics were considered in developing macroscopic models, whereas the principle of demographic gravitation is incorporated in developing microscopic models. Statistical and back-propagation neural network (BPNN) techniques are used in developing the models. The results obtained indicate that statistical and neural network models ensured significantly lower errors. Overall, the BPNN models yielded better results in estimating travel demand and crashes than any other approach considered in this research. The neural network approach can be particularly suitable for their better predictive capability, whereas the statistical models could be used for mathematical formulation or understanding the role of explanatory variables in estimating AADT. Results obtained also indicate that land use characteristics have better predictive capabilities than other variables considered in this research. The outcomes can be used in safety conscious planning, land use decisions, long range transportation plans, prioritization of projects (short term and long term), and, to proactively apply safety treatments

Using machine learning to predict activity chains and mode choice on transportation models

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2020.Considerando as viagens como demanda derivada da necessidade das pessoas de executar suas atividades, fica claro que um melhor entendimento de como as pessoas organizam essas atividades durante o dia leva a uma modelagem de demanda por transportes mais sólida. Replicando decisões desagregadas (individuais) de transporte, os modelos baseados em atividades podem produzir melhores previsões de demanda por viagens comparados às gerações anteriores de abordagens de modelagem (a modelagem baseada em viagens, por exemplo). Um artigo publicado em 2019 se destaca entre as produções científicas recentes relacionadas à modelagem baseada em atividades por propor um modelo composto para geração de diários detalhados de atividades para agentes, com base em suas características socioeconômicas, o Agendador de Atividades Baseado em Dados (Data-Driven Activity Scheduler – DDAS). O objetivo deste trabalho foi desenvolver uma replicação comentada da abordagem metodológica de dois módulos do DDAS: o Modelo de Tipo de Atividade (Activity Type Model – ATM) e o Modelo de Escolha Modal (Mode Choice Model – MCM). Objetivos específicos incluíam a replicação destes módulos do DDAS usando dados da Pesquisa de Mobilidade Urbana do Distrito Federal, que é significativamente maior que a base de dados utilizada no artigo original. Além disso, pretendia-se investigar possíveis melhorias a serem feitas aos modelos do DDAS ou ao seu método de validação. Os resultados obtidos indicaram que uma modificação no método de treino dos modelos poderia compensar o desbalanço de frequência entre as classes. Assim, foi desenvolvida uma segunda implementação usando a técnica de SMOTE (Synthetic Minority Oversampling Technique – Técnica de Sobreamostragem Sintética de Minoria) para treinar os módulos ATM e MCM. Apesar de terem sido obtidas cadeias de atividades mais realistas a partir dessa segunda implementação, o score de validação para o módulo ATM foi baixo. Dessa forma, uma terceira implementação foi desenvolvida, com os modelos treinados como classificadores Random Forest no lugar de classificadores de árvore de decisão isoladas. Foi observada melhoria significativa nos resultados desse terceiro modelo, tanto no treinamento quanto na validação, para ambos os módulos ATM e MCM. Além disso, outra contribuição desse trabalho foi a disponibilização pública de todos os códigos desenvolvidos durante sua condução.When travel is considered a demand derived from people’s need to perform activities, it becomes clear that a better understanding of how people organize their activities during a day must provide a more solid basis for travel demand modeling. By replicating disaggregate travel decisions (at the individual level), activity-based models may produce better travel demand predictions, compared to the previous generations of modeling approaches (tripbased approaches, for instance). A paper published in 2019 stands out among the most recent activity-based modeling research as the authors propose a comprehensive framework for generating full and detailed activity schedules for given agents depending on their sociodemographic features, called Data-Driven Activity Scheduler (DDAS). The aim of this research was to develop a commented replication of the methodological approach of two modules of the DDAS: the Activity Type Model (ATM) and the Mode Choice Model (MCM). Specific objectives included replicating these two modules of the DDAS framework using data from the Federal District Urban Mobility Survey, which is significantly larger than the dataset used in the original DDAS study. Moreover, it was intended to investigate possible improvements to be made on the DDAS framework, including its validation procedure. The obtained results from the replication of the DDAS framework indicated that there was improvement to be made on the manner how models were being trained, in order to better deal with class imbalance. Therefore, a second implementation was made by using the SMOTE technique (Synthetic Minority Oversampling Technique) for training the ATM and MCM modules. Although activity chains seemed more realistic in this second set of results, the overall validation score for the ATM module was low. Therefore, a third model was developed by training the models as Random Forest classifiers instead of isolated Decision Tree classifiers as it was defined in the original DDAS framework. Significant improvement was observed in the results of this third model, both in training and test, for both ATM and MCM modules. Furthermore, another contribution of this study is the public availability of all scripts that were developed during its conduction

Trip distribution modelling using neural network

In this research a new generalized regression neural network (GRNN) model has been researched to estimate the distribution of journey to work trips. As a case study, the model was applied to the journey to work trips in the City of Mandurah in Western Australia. The results of the GRNN model were compared with the well-known doubly-constrained gravity model and the Back-Propagation model and its superiority over these models has been demonstrated

Classification by Neural Network and Statistical Models in Tandem: Does Integration Enhance Performance?

The major purposes of the current research are twofold. The first purpose is to present a composite approach to the general classification problem by using outputs from various parametric statistical procedures and neural networks. The second purpose is to compare several parametric and neural network models on a transportation planning related classification problem and five simulated classification problems

Prospects for Research in Transport and Logistics on a Regional: Global Perspective (I: February 2009: İstanbul: Turkey)

"International Conference on Prospects for Research in Transport and Logistics on a Global - Regional Perspective" has undertaken the challenge to host very important experts and practitioners of Transport and Logistics from a large spectrum of countries. In our opinion, the conference has fulfilled the purpose of establishing an International Society; "Eurasian and Eastern Mediterranean Institute of Transportation and Logistics Association (EMIT)" that is expected to have a very promising role in the Eurasian and Eastern Mediterranean countries. The purpose of the Association is to contribute to establishing and developing the exchange of research work between all parts of the world in all fields of transportation and logistics. This proceedings book consists of 13 chapters, grouping the contributed papers into the following categories: Global Issues in Logistics and Transportation (3 papers), Regional Issues in Logistics and Transportation (2 papers), Education and Training in Logistics and Transportation (2 papers), Supply Chain Management (3 papers), Sustainable Transport Policies, Traffic Engineering (4 papers), Evaluation of Public Policies, Network Models and Environment (4 papers), Contemporary Topics in Transport and Logistics (7 papers), Transport Planning and Economics (3 papers), Planning, Operations, Management and Control of Transport and Logistics (3 papers), Transport Modeling (5 papers), Freight Transportation and Logistics Management (7 papers), Transport and Land Use (3 papers), Transport Infrastructure and Investment Appraisal (2 papers) It can be readily seen from this volume of selected papers that all papers do elaborate on rather timely problems in the fields of expertise related to Transport and Logistics, which have a considerable global importance.TÜBİTAK; Doğuş Üniversitesi ; Uluslararası Nakliyeciler Derneği ; İDO ; Tırsan ; Türk Hava YollarıCommittees, i -- Words of Welcome and Gratitude, ii -- Introduction, iii -- Chapter 1 Global Issues in Logistics and Transportation, 1 -- Potential to Reduce GHG through Efficient Logistic Concepts, 3 -- Werner Rothengatter -- A methodological framework for the evaluation and prioritisation of multinational transport projects: the Case of euro-asian transport linkages, 21 / Dimitrios TSAMBOULAS, Angeliki KOPSACHEILI -- Container port throughput performance - case study: Far east, north west european and mediterranean ports, 29 / Vesna DRAGOVIC-RADINOVIC, Branislav DRAGOVIC, Maja SKURIC, EmirĞIKMIROVlC and Ivan KRAPOVIC -- Chapter 2 Regional Issues in Logistics and Transportation, 35 -- Logistics service providers in turkey: A panel data analysis, 37 / Emel AKTAŞ, Füsun ÜLENGİN, Berrin AĞARAN, Şule ÖNSEL -- Milestones in the process of survey preparation for the logistics sector: case study for Istanbul, Turkey, 43 / Evren POSACI, Darçın AKIN -- Chapter 3 Education and Training in Logistics and Transportation, 51 -- Education in transport and logistics in an age of global economy, 53 / Yücel Candemlr -- The role of education and training in the supply chain sector, 59 / David Maunder -- Chapter 4 Supply Chain Management, 64 -- Modeling reverse flows in a closed -loop supply chain network, 67 / Vildan ÖZKIR, Önder ÖNDEMİR and Hüseyin BAŞLIGİL -- Strategic analysis of green supply chain management practices in T urkish automotive industry, 73 / Gülçin BÜYÜKÖZKAN and Alişan ÇAPAN -- A new framework for port competitiveness: the network approach, 79 / Marcella DE MARTINO, Alfonso MORVILLO -- Chapter 5 Sustainable Transport Policies, Traffic Engineering, 87 -- Clean transport: innovative solutions to the creation of a more sustainable urban transport system, 89 / Ela BABALIK-SUTCLIFFE -- Effects of urban bottlenecks on highway traffic congestion: case study of Istanbul, Turkey, 95 / Darçın AKIN and Mehtap ÇELİK -- Establishing an effective training module for IMDG code in MET institutions, 105 / Kadir CICEK, Metin CELIK -- An investment decision aid proposal towards choice of container terminal operating systems based on information axioms, 109 / Metin CELIK, Selcuk CEBI -- Chapter 6 Evaluation of Public Policies, Network Models and Environment, 115 -- Possibilistic linear programming approach for strategic resource planning, 117 / Özgür KABAK, Füsun ÜLENGİN -- A structural equation model for measuring service quality in passenger transportation, 125 / G.Nilay YÜCENUR and Nihan ÇETİN DEMİREL -- Analysis of potential gain from using hybrid vehicles in public transportation, 133 / İrem DÜZDAR and Özay ÖZAYDIN -- Optimization of e-waste management in Marmara region - Turkey, 141 / İlke BEREKETLİ, Müjde EROL GENEVOIS -- Chapter 7 Contemporary Topics in Transport and Logistics, 147 -- Future prospects on urban logistic research, 149 / Rosârio MACÂRIO, Vasco REIS -- An analyze of relationship between container ships and ports development, 155 / Branislav DRAGOVIC, Vesna Dragovic-Radinovic, Dusanka Jovovic, Romeo Mestrovic and Emir Ğikmirovic -- A holistic framework for performance measurement in logistics management, 161 / Yasemin Claire ERENSAL -- Heuristics for a generalization of tsp in the context of PCB assembly, 167 / Ali Fuat ALKAYA and Ekrem DUMAN -- Premium e-grocery: Exploring value in logistics integrated service solutions, 173 / Burçin BOZKAYA, Ronan De KERVENOAEL and D. Selcen Ö. AYKAÇ -- T ravelers response to VMS in the Athens area, 179 / Athena TSIRIMPA and Amalia POLYDOROPOULOU -- Regional airports and local development: the challenging balance between sustainability and economic growth, 189 / Rosârio MACÂRIO and Jorge SILVA -- Chapter 8 Transport Planning and Economics, 195 -- How financial constraints and non-optimal pricing affect the design of public transport services, 197 / Sergio R. Jara-Diaz and Antonio Gschwender -- Revenue management for returned products in reverse logistics, 203 / Mesut KUMRU -- Intra-city bus planning using computer simulation, 211 / Reza AZIMI and Amin ALVANCHI -- Chapter 9 Planning, Operations, Management and Control of Transport and Logistics, 217 -- A review of timetabling and resource allocation models for light-rail transportation systems, 219 / Selmin D. ÖNCÜL, D. Selcen Ö. AYKAÇ, Demet BAYRAKTAR and Dilay ÇELEBİ -- An approach of integrated logistics HMMS model under environment constraints and an application of time scale, 225 / Fahriye Uysal, Ömür Tosun, Orhan Kuruüzüm -- Freight transport planning with genetic algorithm based projected demand, 231 / Soner HALDENBILEN, Ozgur BASKAN, Huseyin CEYLAN and Halim CEYLAN -- Chapter 10 Transport Modeling, 239 -- Inverse model to estimate o-d matrix from link traffic counts using ant colony optimization, 241 / Halim CEYLAN, Soner HALDENBILEN, Huseyin CEYLAN, Ozgur BASKAN -- The impact of logistics on modelling commercial freight traffic, 251 / Ute IDDINK and Uwe CLAUSEN -- A comparative reviewof simulation-based behavior modeling for travel demand generation, 257 / Seda Yanık, Mehmet Tanyaş -- An efficiency analysis of turkish container ports using the analytic network process, 269 / Senay OĞUZTİMUR, Umut Rıfat TUZKAYA -- A multi-objective approach to designing a multicommudity supply chain distribution network with multiple capacities, 277 / Gholam Reza Nasiri, Hamid Davoudpour and B.Karimi -- Chapter 11 Freight Transportation and Logistics Management, 283 -- Evaluation of turkey’s freight transportation, 285 / Burcu KULELİ PAKand BaharSENNAROĞLU -- Short sea shipping, intermodality and parameters influencing pricing policies in the Mediterranean region: The Italian context, 291 / Monica GROSSO, Ana-Rita LYNCE, Anne SILLA, Georgios K. VAGGELAS -- Relevant strategic criteria when choosing a container port - the case of the port of Genoa, 299 / Monica Grosso, Feliciana Monteiro -- Determination of optimum fleet size and composition - A case study of retailer in Thailand, 307 / Terdsak RONGVIRIYAPANICH and Kawee SRIMUANG -- New container port development: forecasting future container throughput, 313 / Dimitrios TSAMBOULAS, Panayota MORAITI -- Sea port hinterland flows and opening hours: the way forward to make them match better 319 / Marjan BEELEN, Hilde MEERSMAN, Evy ONGHENA, Eddy VAN DE VOORDE and Thierry VANELSLANDER -- International road freight transport in Germany and the Netherlands driver costs analysis and French perspectives, 327 / Laurent GUIHERY -- Chapter 12 Transport and Land Use, 335 -- Land rent and new transport infrastructure: How to manage this relationship?, 337 / Elena SCOPEL -- Effects of pavement characteristics on the traffic noise levels, 345 / Aybike ONGEL and John HARVEY -- Fuzzy medical waste disposal facility location problem, 351 / Yeşim KOP, Müjde EROL GENEVOIS and H. Ziya ULUKAN -- Chapter 13 T ransport Infrastructure and Investment Appraisal, 357 -- Agents’ behavior in financing Italian transport infrastructures, 359 / Paolo BERIA -- Free trade agreements in the mediterranean region: a box-cox analysis, 367 / Matthew KARLAFTIS, Konstantinos KEPAPTSOGLOU and Dimitrios TSAMBOULA