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1
Hatzopoulou, Marianne, Eric J. Miller, and Bruno Santos. "Integrating Vehicle Emission Modeling with Activity-Based Travel Demand Modeling." Transportation Research Record: Journal of the Transportation Research Board 2011, no. 1 (January 2007): 29–39. http://dx.doi.org/10.3141/2011-04.
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Recker, W. W. "A bridge between travel demand modeling and activity-based travel analysis." Transportation Research Part B: Methodological 35, no. 5 (June 2001): 481–506. http://dx.doi.org/10.1016/s0191-2615(00)00006-0.
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Lekshmi, G. R. Amrutha, V. S. Landge, and V. S. Sanjay Kumar. "Activity Based Travel Demand Modeling of Thiruvananthapuram Urban Area." Transportation Research Procedia 17 (2016): 498–505. http://dx.doi.org/10.1016/j.trpro.2016.11.100.
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Pendyala, Ram M., Ryuichi Kitamura, Akira Kikuchi, Toshiyuki Yamamoto, and Satoshi Fujii. "Florida Activity Mobility Simulator." Transportation Research Record: Journal of the Transportation Research Board 1921, no. 1 (January 2005): 123–30. http://dx.doi.org/10.1177/0361198105192100114.
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The development of modeling systems for activity-based travel demand ushers in a new era in transportation demand forecasting and planning. A comprehensive multimodal activity-based system for forecasting travel demand was developed for implementation in Florida and resulted in the Florida Activity Mobility Simulator (FAMOS). Two main modules compose the FAMOS microsimulation model system for modeling activity–travel patterns of individuals: the Household Attributes Generation System and the Prism-Constrained Activity–Travel Simulator. FAMOS was developed and estimated with household activity and travel data collected in southeast Florida in 2000. Results of the model development effort are promising and demonstrate the applicability of activity-based model systems in travel demand forecasting. An overview of the model system, a description of its features and capabilities, and preliminary validation results are provided.
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Hilgert, Tim, Michael Heilig, Martin Kagerbauer, and Peter Vortisch. "Modeling Week Activity Schedules for Travel Demand Models." Transportation Research Record: Journal of the Transportation Research Board 2666, no. 1 (January 2017): 69–77. http://dx.doi.org/10.3141/2666-08.
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Activity schedules are an important input for travel demand models. This paper presents a model to generate activity schedules for one week. The approach, called actiTopp, is based on the concept of utility-based regression models and stepwise modeling. In contrast to most of the existing models, actiTopp covers the time period of one week. Few models have covered one week; thus, the activity generation approach of this simulation period is rare. Analysis of weekly activity behavior shows stability between different days (e.g., working durations). Hence, the model explicitly takes these aspects into account, for example, by defining time budgets to spread durations within the week. For model estimation, the study used data from the German Mobility Panel (MOP). This annual survey collects representative data on the travel behavior of the German population. The data from 2004–2013 provide more than 17,500 activity schedules for one week, with more than 450,000 activities. Selected results are shown for the model application to 2014 MOP data, which the study used for validation purposes. The mean value of activities per person and week show a difference of 0.3 activity. To evaluate the model, the study used Kolmogorov-Smirnov tests with a significance level of α = 0.001. For the activity type distribution of the 2014 sample, the analysis could not reject the null hypothesis of equality of the distribution of the model and the survey data at this significance level.
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Bao, Qiong, Bruno Kochan, Tom Bellemans, Yongjun Shen, Lieve Creemers, Davy Janssens, and Geert Wets. "Travel Demand Forecasting Using Activity-Based Modeling Framework FEATHERS: An Extension." International Journal of Intelligent Systems 30, no. 8 (April 11, 2015): 948–62. http://dx.doi.org/10.1002/int.21733.
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Hafezi, Mohammad Hesam, Lei Liu, and Hugh Millward. "A time-use activity-pattern recognition model for activity-based travel demand modeling." Transportation 46, no. 4 (November 20, 2017): 1369–94. http://dx.doi.org/10.1007/s11116-017-9840-9.
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Delhoum, Younes, Rachid Belaroussi, Francis Dupin, and Mahdi Zargayouna. "Activity-Based Demand Modeling for a Future Urban District." Sustainability 12, no. 14 (July 20, 2020): 5821. http://dx.doi.org/10.3390/su12145821.
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Identifying the spatio-temporal patterns of people activities in urban areas is key to effective urban planning; it can be used in real-estate projects to predict their future impacts on behavior in surrounding accessible areas. LaVallée is a large construction project recently started in Paris’s suburb; it is a new district due in 2024. The paper is in the field of urban planning, aiming at developing a method making it possible to model the potential visits of the various equipment and public spaces of the district, by mobilizing data from census at the departmental level, and the layout of shops and activities as defined by the real-estate project. This model takes into account the flow of external visitors, estimated realistically based on the pre-project movements in the areas of influence of LaVallée. In this paper, we propose an activity-based model methodology to determine trips and their purpose at a mesoscopic scale including the city and surrounding areas, in the current baseline scenario. This travel demand is required to estimate potential external visitors of the future district. A first demonstration shows that the model correctly represents the current demands and allows the forecast of future demand in the area.
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Lim, Kwang-Kyun, Sigon Kim, and SungBong Chung. "Activity-based Approaches for Travel Demand Modeling: Reviews on Developments and Implementations." Journal of The Korean Society of Civil Engineers 33, no. 2 (March 30, 2013): 719–27. http://dx.doi.org/10.12652/ksce.2013.33.2.719.
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Bao, Qiong, Bruno Kochan, Yongjun Shen, Tom Bellemans, Davy Janssens, and Geert Wets. "Activity-Based Travel Demand Modeling Framework FEATHERS: Sensitivity Analysis with Decision Trees." Transportation Research Record: Journal of the Transportation Research Board 2564, no. 1 (January 2016): 89–99. http://dx.doi.org/10.3141/2564-10.
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Dhakar, Nagendra, Joel Freedman, Mark Bradley, and Wu Sun. "Pricing and Reliability Enhancements in the San Diego, California, Activity-Based Travel Model." Transportation Research Record: Journal of the Transportation Research Board 2669, no. 1 (January 2017): 19–30. http://dx.doi.org/10.3141/2669-03.
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The estimation of demand for priced highway lanes is becoming increasingly important to agencies seeking to improve mobility and find alternative revenue sources for the provision of transportation infrastructure. However, many modeling tools fall short of what is required for robust estimates of demand with respect to toll and managed lanes in two key areas: the value of time is often aggregate and not consistently defined throughout the model system, and the reliability of transport infrastructure is rarely considered. This paper describes an effort that implemented recommendations of the Strategic Highway Research Program on pricing and reliability within a regional activity-based modeling system for the San Diego, California, region. The implemented recommendations included distributed travel time sensitivities across the synthetic population and special travel markets, continuous cost sensitivity on the basis of income, and multiple value of time bins in highway skimming and assignment. The work also included innovative research related to the analysis of travel time variability on the basis of a temporally disaggregate (1-min interval) data set of automobile travel speeds for most automobile links in the San Diego network for the month of October 2012. Regression equations that related the travel time reliability to link characteristics, incorporated reliability in automobile travel skims, incorporated those skims in the travel demand model system, and calculated toll elasticity on toll roads in San Diego County were estimated. The enhanced model matched observed toll demand better than the original model. Resulting elasticity values were generally found to be in the ranges reported in the literature.
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Miller, Eric J. "Modeling the Demand for New Transportation Services and Technologies." Transportation Research Record: Journal of the Transportation Research Board 2658, no. 1 (January 2017): 1–7. http://dx.doi.org/10.3141/2658-01.
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This paper briefly discusses the current state of the art of urban travel demand modeling and research needs in the field. Special emphasis is given to both the challenges and the opportunities posed by modern information technology and the data, transportation services, and travel behaviors that this technology is generating. Travel demand modeling has made very significant strides over the past 20 years, especially in the development of operational activity- and tour-based regional travel demand forecasting systems. These model systems represent first-generation agent-based microsimulation models. Considerable need, opportunity, and scope exist for the development of significantly more powerful second-generation agent-based microsimulation models that build upon emerging big data sets (among other information sources) and high-performance computing. This task, however, will involve the development of new behavioral representations and computational algorithms implemented within much more flexible software environments that both fully exploit available computing power and enable flexible experimentation with and extension of representations of new transportation modes and services and evolving travel behavior.
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Davidson, William, Robert Donnelly, Peter Vovsha, Joel Freedman, Steve Ruegg, Jim Hicks, Joe Castiglione, and Rosella Picado. "Synthesis of first practices and operational research approaches in activity-based travel demand modeling." Transportation Research Part A: Policy and Practice 41, no. 5 (June 2007): 464–88. http://dx.doi.org/10.1016/j.tra.2006.09.003.
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Chen, Xuwei. "Activity-based Modeling and Microsimulation of Emergency Evacuations." International Journal of Applied Geospatial Research 6, no. 3 (July 2015): 21–38. http://dx.doi.org/10.4018/ijagr.2015070102.
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Most existing emergency evacuation studies assume evacuees to evacuate from their residence locations. However, depending on the time of day, people's movements are constrained not only by their workplaces, but also the necessity of picking up family members. Family member interactions, a typical activity during an evacuation, can greatly affect the evacuation process. Activity-based modeling has been applied to estimate daily traffic demand widely. However, only limited research has been reported to incorporate the activity component in examining evacuation processes, particularly for mass evacuations at a micro-scale. Under this context, this study aims to analyze the activity-based travel pattern and its impact on emergency evacuations in the case of a hypothetical emergency evacuation of Galveston Island, Texas. In the study, one typical type of daily trip, picking up school-age children, is considered. All households with school-age children are assumed to pick up their children first in an evacuation. Trip chains are defined to represent the movements from workplaces to schools and then to destinations. This study employs agent-based microsimulation techniques to model the evacuation process at the individual-driver level. The simulation results suggest that the overall evacuation time may not be significantly affected when the trips of picking-up school-age children are considered in the case of Galveston Island. However, the average travel and delay time of individual vehicles may increase dramatically, which suggests the occurrence of considerable congestions during the evacuation. The findings demonstrated the importance of considering activity-based trips in evacuations.
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Vovsha, Peter, Eric Petersen, and Robert Donnelly. "Explicit Modeling of Joint Travel by Household Members: Statistical Evidence and Applied Approach." Transportation Research Record: Journal of the Transportation Research Board 1831, no. 1 (January 2003): 1–10. http://dx.doi.org/10.3141/1831-01.
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A substantial portion of regional travel is implemented by household members who travel together, primarily to participate in a shared household activity. Joint household travel is not explicitly accounted for in most regional travel models in which the unit of travel (either trip or tour) is considered for each person separately at each modeling stage—generation, mode, destination, and time-of-day choice. In addition, statistical evidence demonstrates that the vast majority of shared-ride travel consists of joint household travel. A modeling approach that distinguishes shared activity-based joint household travel from arranged interhouse-hold carpooling is clearly desirable to support accurate forecasts of shared-ride travel, critical in the evaluation of high-occupancy vehicle lanes or the adoption of toll strategies differentiated by occupancy levels. A range of aspects of joint travel both with empirical evidence and with discussion of modeling issues are addressed. A set of joint travel models is presented that has been estimated with the mid-Ohio regional travel household-interview survey. The model reported is one of the innovative components of the tour-based travel demand modeling system that has been developed for the Mid-Ohio Regional Planning Commission.
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Korimilli, Madhuri S., Ram M. Pendyala, and Elaine Murakami. "Metaanalysis of Travel Survey Methods." Transportation Research Record: Journal of the Transportation Research Board 1625, no. 1 (January 1998): 72–78. http://dx.doi.org/10.3141/1625-09.
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Travel surveys often serve as the primary sources of information on travel demand characteristics. They provide critical data for transportation planning and decision making. In recent times, several factors motivate a comparative examination of travel survey methods. First, new travel demand modeling tools, such as those based on activity-based methods, are placing greater demands on travel behavior data gathered from household travel surveys. Second, response rates from household travel surveys have been showing a steady decline, possibly because of an increasingly survey-fatigued population. Third, declining resource availability at metropolitan planning agencies places emphasis on the need to maximize response rates to lower data collection costs per completed respondent. Ideally, a comparative examination of travel survey methods is best done through a carefully constructed experimental design that permits the isolation of the impact of various survey design parameters on response rates. However, the conduct of such a controlled experiment virtually is impractical. A metaanalysis of a sample of travel surveys conducted in the past 10 years is presented. A predictive model of response rates is developed by using linear regression techniques and the practical application of the model is demonstrated through several numerical examples.
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Viegas de Lima, Isabel, Mazen Danaf, Arun Akkinepally, Carlos Lima De Azevedo, and Moshe Ben-Akiva. "Modeling Framework and Implementation of Activity- and Agent-Based Simulation: An Application to the Greater Boston Area." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 49 (October 1, 2018): 146–57. http://dx.doi.org/10.1177/0361198118798970.
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This paper presents a utility-maximizing approach to agent-based modeling with an application to the Greater Boston Area (GBA). It leverages day activity schedules (DAS) to create a framework for representing travel demand in an individual’s day. DAS are composed of a sequence of stops that make up home-based tours with activity purposes, intermediate stops, and subtours. The framework introduced in this paper includes three levels: (1) the Day Pattern Level, which determines if an individual will travel and, if so, what types of primary activities and intermediate stops they will do; (2) the Tour Level, which models the mode, destination, and time-of-day of the different primary activities; and (3) the Intermediate Stop Level, which generates intermediate stops. The models are estimated for the GBA using the 2010 Massachusetts Travel Survey (MTS). They are then implemented in SimMobility, the agent-based, activity-based, multimodal simulator. It run in a microsimulation using a Synthetic Population. Produced results are consistent with the MTS. Compared with similar activity-based approaches, the proposed framework allows for more flexibility in modeling a wide range of activity and travel patterns.
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Badoe, Daniel A., and Chin-Cheng Chen. "Unit of analysis in conventional trip generation modelling: an investigation." Canadian Journal of Civil Engineering 31, no. 2 (February 1, 2004): 272–80. http://dx.doi.org/10.1139/l03-098.
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This paper examines the importance of the unit of analysis selected for trip generation modelling when the model estimation data are collected in a household travel survey. The paper reviews the literature on the arguments made for the use of the "individual" or the "household" as the unit of analysis in trip production modelling, and then through a statistical exposition it determines what should be the appropriate unit of analysis. An empirical test of the forecast performance of household- and person-trip generation models is conducted using data collected in a household-travel-behaviour survey in the Greater Toronto Area of Canada. The paper concludes that the household is theoretically the preferable analysis unit to use in trip production modelling when the model estimation data are collected in a household travel survey in which the household is the sampling unit. The empirical test indicates that household-trip generation models yield predictions of trips at the household and traffic zone level, respectively, that are marginally more accurate than those yielded by person-trip generation models.Key words: trip generation, travel demand forecasting, household trip generation, person trip generation, sampling unit, travel demand modeling, activity-based travel forecasting.
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P. Alex, Anu. "Modelling of Activity-Travel Pattern with Support Vector Machine." European Transport/Trasporti Europei, no. 82 (June 2021): 1–15. http://dx.doi.org/10.48295/et.2021.82.2.
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Activity based travel demand modelling involves lot of uncertainty due to the complex and varying decision making behaviour of each individual. This study contributes to the literature by assessing the suitability of Support Vector Machine (SVM) in modelling the activity pattern and travel behaviour of workers. Activity and travel behaviour of workers consists of decision outcomes, which can be modelled as classification and regression problems. SVM is a good classifier and regressor with good testing and learning capability, hence the present study used SVM for modelling. It was found that support vector machine models are well performing to predict the activity pattern and travel behaviour of workers. The SVM models developed in the study predicts the temporal variation of mode wise work activity generation. Prediction of temporal mode share of commuters is advantageous to policy makers to experiment the implementation of temporary Travel Demand Management (TDM) actions effectively.
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Ballis, Haris, and Loukas Dimitriou. "Deriving Daily Activity Schedules from Dynamic, Purpose-Dependent Origin–Destination Matrices." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (August 5, 2020): 375–86. http://dx.doi.org/10.1177/0361198120939094.
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Emerging transport modeling approaches such as agent/activity-based modeling as well as the shift toward data-driven paradigm have stressed the need for high-quality travel behavior data at a disaggregate level. Despite the advances in personal mobility tracking and capturing, aggregate methods such as origin–destination (OD) matrices are still the most widespread means to organize and represent travel demand information. Nonetheless, traditional ODs cannot directly capture substantial elements which can considerably affect travel behavior (e.g., trip interdependency, trip chaining, etc.) therefore they are not the most suitable mean to facilitate relevant analysis. The currently presented framework alleviates this limitation by combining the individual trips present in multi-period, purpose-dependent OD matrices into sequences of trips originating and ending at users’ home location (i.e., tours). This is achieved by integrating graph-theoretical with combinatorial optimization concepts. A graph theory-based methodology is applied to first examine the spatiotemporal information in ODs and then identify all the plausible tours. Then the sequence of activities (i.e., daily activity schedule [DAS]) taking place during each diurnal tour is inferred based on the trip-purpose information contained in the original ODs. Finally, a combinatorial optimization routine identifies the optimum combination of DAS whose aggregated trips represent the total travel demand as observed in the OD matrices. The resulting DAS by the proposed estimation framework has proven to be of significant accuracy and provide valuable information in relation to the characteristics of the population’s travel behavior within the urban space environment. This information is particularly useful to mitigate against the upcoming challenges in the field of smart mobility management.
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Hafezi, Mohammad Hesam, Lei Liu, and Hugh Millward. "Identification of Representative Patterns of Time Use Activity Through Fuzzy C-Means Clustering." Transportation Research Record: Journal of the Transportation Research Board 2668, no. 1 (January 2017): 38–50. http://dx.doi.org/10.3141/2668-05.
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Analysis of the time use activity patterns of urbanites will contribute greatly to the modeling of urban transportation demands by linking activity generation and activity scheduling modules in the overall activity-based modeling framework. This paper develops a framework for novel pattern recognition modeling to identify groups of individuals with homogeneous daily activity patterns. The framework consists of four modules: initialization of the total cluster number and cluster centroids, identification of individuals with homogeneous activity patterns and grouping of them into clusters, identification of sets of representative activity patterns, and exploration of interdependencies among the attributes in each identified cluster. Numerous new machine-learning techniques, such as the fuzzy C-means clustering algorithm and the classification and regression tree classifier, are employed in the process of pattern recognition. The 24-h activity patterns are split into 288 intervals of 5-min duration. Each interval includes information on activity types, duration, start time, location, and travel mode, if applicable. Aggregated statistical evaluation and Kolmogorov–Smirnov tests are performed to determine statistical significance of clustered data. Results show a heterogeneous diversity in eight identified clusters in relation to temporal distribution and significant differences in a variety of sociodemographic variables. The insights gained from this study include important information on activities—such as activity type, start time, duration, location, and travel distance—that are essential for the scheduling phase of the activity-based model. Finally, the results of this paper are expected to be implemented within the activity-based travel demand model for Halifax, Nova Scotia.
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Alex, Anu P., V. S. Manju, and Kuncheria P. Isaac. "Modelling of travel behaviour of students using artificial intelligence." Archives of Transport 51, no. 3 (September 30, 2019): 7–19. http://dx.doi.org/10.5604/01.3001.0013.6159.
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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.
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Erhardt, Gregory D., David L. Kurth, Erik E. Sabina, and Smith Myung. "Market-Based Framework for Forecasting Parking Cost in Traditional and Microsimulation Modeling Applications." Transportation Research Record: Journal of the Transportation Research Board 1921, no. 1 (January 2005): 79–88. http://dx.doi.org/10.1177/0361198105192100110.
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Parking cost is an important variable in determining mode choice, yet it receives little attention in most travel forecasting models. This paper presents a framework for modeling parking supply and cost that has three advantages over most parking cost models: a market-based approach is used to equilibrate parking demand with parking supply; actual parking costs paid by groups of travelers rather than average parking costs are estimated for each transportation analysis zone; and estimates are made from longitudinal data. This framework has been applied successfully in a traditional four-step travel model and is being used in practice. It also provides additional opportunities for application in a segmented manner or in concert with a microsimulation modeling approach. Mode choice results based on aggregate and segmented applications of the framework are substantially different. Improved forecasting of parking costs should be an important consideration in any new model development. In recent years, substantial efforts have been focused on household interactions and activity modeling. Although the understanding of travel behavior has improved substantially, the improved techniques still depend on good input data for credible forecasts.
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Bao, Qiong, Yongjun Shen, Lieve Creemers, Bruno Kochan, Tom Bellemans, Davy Janssens, and Geert Wets. "Investigating the Minimum Size of Study Area for an Activity-Based Travel Demand Forecasting Model." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/162632.
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Nowadays, considerable attention has been paid to the activity-based approach for transportation planning and forecasting by both researchers and practitioners. However, one of the practical limitations of applying most of the currently available activity-based models is their computation time, especially when large amount of population and detailed geographical unit level are taken into account. In this research, we investigated the possibility of restraining the size of the study area in order to reduce the computation time when applying an activity-based model, as it is often the case that only a small territory rather than the whole region is the focus of a specific study. By introducing an accuracy level of the model, we proposed in this research an iteration approach to determine the minimum size of the study area required for a target territory. In the application, we investigated the required minimum size of the study area surrounding each of the 327 municipalities in Flanders, Belgium, with regard to two different transport modes, that is, car as driver and public transport. Afterwards, a validation analysis and a case study were conducted. All the experiments were carried out by using the FEATHERS, an activity-based microsimulation modeling framework currently implemented for the Flanders region of Belgium.
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Yagi, Sadayuki, and Abolfazl (Kouros) Mohammadian. "An Activity-Based Microsimulation Model of Travel Demand in the Jakarta Metropolitan Area." Journal of Choice Modelling 3, no. 1 (2010): 32–57. http://dx.doi.org/10.1016/s1755-5345(13)70028-9.
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Huang, Ling, and Jian Ping Wu. "A General Traveler Agent Behaviour Model for Traffic Flow Simulation." Key Engineering Materials 467-469 (February 2011): 1156–59. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1156.
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The modeling of traveler’s daily travel behaviour is a complex problem. We propose a general activity-based traveler agent behaviour model for microscopic traffic flow simulation, inspired by the ideas of activity-based traffic demand model, hierarchical structure in behaviours, agent approach and subjective utility optimization method. In the case study, the general model was applied to an unsignalized intersection mixed traffic flow simulation model, and the validation results were promising.
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Zhong, Ming, Rong Shan, Donglei Du, and Chunyu Lu. "A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa, Florida." Transportation Planning and Technology 38, no. 5 (June 4, 2015): 517–33. http://dx.doi.org/10.1080/03081060.2015.1039232.
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Wets, Geert, Koen Vanhoof, Theo Arentze, and Harry Timmermans. "Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms." Transportation Research Record: Journal of the Transportation Research Board 1718, no. 1 (January 2000): 1–9. http://dx.doi.org/10.3141/1718-01.
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The utility-maximizing framework—in particular, the logit model—is the dominantly used framework in transportation demand modeling. Computational process modeling has been introduced as an alternative approach to deal with the complexity of activity-based models of travel demand. Current rule-based systems, however, lack a methodology to derive rules from data. The relevance and performance of data-mining algorithms that potentially can provide the required methodology are explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice in the context of activity scheduling from a large activity diary data set. The algorithm is compared with both an alternative method of inducing decision trees (CHAID) and a logit model on the basis of goodness-of-fit on the same data set. The ratio of correctly predicted cases of a holdout sample is almost identical for the three methods. This suggests that for data sets of comparable complexity, the accuracy of predictions does not provide grounds for either rejecting or choosing the C4 method. However, the method may have advantages related to robustness. Future research is required to determine the ability of decision tree-based models in predicting behavioral change.
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Gao, Liangpeng, Yanjie Ji, Yang Liu, and Baohong He. "Research on Modeling Intrahousehold Interactions from the Perspective of Space-Time Constraints." Discrete Dynamics in Nature and Society 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2917106.
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Interactions among family members can yield valuable information for interpreting individual travel decisions. Typically, each family member plays a set role and travel decisions are made by considering the combined needs of household members. This study investigates both multiactivity and multiperson interactions in urban nuclear families and proposes the novel concepts of “activity-restriction degree” and “activity-constraint niche” to quantify the degree of space-time constraints within time geography. A structural equation model is employed to analyze intrahousehold interactions based on individual activity-travel patterns during the workday. The results indicate that the links between family members reflect behavioral responses (with constraints) between individuals and other family members. Household interaction constraints not only influence individual travel decisions but also affect the realization of the household activity for everyone. These interactions lead to reasonable adjustments and mutual support and to the identification of efficient activity patterns that meet the demands of the entire household.
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Skabardonis, Alexander. "Modeling Framework for Estimating Emissions in Large Urban Areas." Transportation Research Record: Journal of the Transportation Research Board 1587, no. 1 (January 1997): 85–95. http://dx.doi.org/10.3141/1587-10.
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A modeling framework has been developed to produce detailed emission inventories for large urban areas. Relationships were developed between the time spent in each driving mode (cruise, acceleration, deceleration, and idle) and basic link characteristics based on simulations of selected real-world surface street networks and freeway sections using the TRAF-NETSIM and INTRAS microscopic models, supplemented by field data. These relationships were then incorporated into a specially written computer program as a postprocessor to the Urban Transportation Planning System type of four-step travel demand models. The integrated model was successfully applied to the 1,120-zone Metropolitan Transportation Commission San Francisco Bay Area network to generate vehicle activity estimates.
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Delhoum, Younes, Rachid Belaroussi, Francis Dupin, and Mahdi Zargayouna. "Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area." Applied Sciences 11, no. 16 (August 20, 2021): 7654. http://dx.doi.org/10.3390/app11167654.
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In their daily activity planning, travelers always considers time and space constraints such as working or education hours and distances to facilities that can restrict the location and time-of-day choices of other activities. In the field of population synthesis, current demand models lack dynamic consistency and often fail to capture the angle of activity choices at different times of the day. This article presents a method for synthetic population generation with a focus on activity-time choice. Activity-time choice consists mainly in the activity’s starting time and its duration, and we consider daily planning with some mandatory home-based activity: the chain of other subsequent activities a traveler can participate in depends on their possible end-time and duration as well as the travel distance from one another and opening hours of commodities. We are interested in a suburban area with sparse data available on population, where a discrete choice model based on utilities cannot be implemented due to the lack of microeconomic data. Our method applies activity-hours distributions extracted from the public census, with a limited corpus, to draw the time of a potential next activity based on the end-time of the previous one, predicted travel times, and the successor activities the agent wants to participate in during the day. We show that our method is able to construct plannings for 126k agents over five municipalities, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, which fit adequately real-world time distributions.
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Ettema, Dick, Aloys Borgers, and Harry Timmermans. "SMASH (Simulation Model of Activity Scheduling Heuristics): Some Simulations." Transportation Research Record: Journal of the Transportation Research Board 1551, no. 1 (January 1996): 88–94. http://dx.doi.org/10.1177/0361198196155100112.
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Travel decision making is increasingly regarded as a highly complex process in which individuals not only decide about frequency of trips, travel modes, and routes, but also about activity participation and sequencing and timing and duration of activities and trips. This raises the question of whether or not traditional discrete-choice models still provide the best starting point for realistically modeling such a process. Some scholars consider computational process models (CPMs) a promising approach because they allow for heuristic search and suboptimal reasoning processes, which are typical for complex decision making. A model of activity scheduling, SMASH (Simulation Model of Activity Scheduling Heuristics), which incorporates aspects of discrete-choice modeling and CPMs, has been proposed. The model describes the pretrip planning phase, in which individuals decide which activities to perform, at what locations, at what times, in which sequence, and how to travel to the various activity sites. The calibration of this model, using data collected with the interactive computerized procedure MAGIC, has been described in the literature. The results indicated that when scheduling their activities, subjects seem to trade off attributes of activities (time constraints, duration), attributes of the schedule (time spent on activities, overall travel time, realism) and characteristics of the scheduling process (amount of effort already involved in the scheduling process) to obtain feasible schedules. More extensive tests, using simulation experiments, of the model's internal, predictive, and face validity are described. SMASH was used to predict subjects' activity schedules based on their activity agenda and information about their spatio-temporal circumstances. The predicted schedules were then compared with the activity schedules conceived by the subjects themselves under different circumstances, to assess the model's validity. The tests indicated that the model provided satisfactory results with respect to the reproduction of the observed activity schedules. The results of the validity test warrant the use of the model for assessing the effects of various policy measures such as time policies, land use policies, and travel demand management.
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Wu, Yizheng, and Guohua Song. "The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure." International Journal of Environmental Research and Public Health 16, no. 18 (September 7, 2019): 3291. http://dx.doi.org/10.3390/ijerph16183291.
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Quantifying the air pollution and health impacts of transportation plans provides decision makers with valuable information that can help to target interventions. However, a large number of environmental epidemiological research assumes exposures of static populations at residential locations and does not consider the human activity patterns, which may lead to significant estimation errors. This study uses an integrated modeling framework to predict fine-grained air pollution exposures occurring throughout residents’ activity spaces. We evaluate concentrations of fine particulate matter (PM2.5) under a regional transportation plan for Sacramento, California, using activity-based travel demand model outputs, vehicle emission, and air dispersion models. We use predicted air pollution exposures at the traffic analysis zone (TAZ) level to estimate residents’ exposure accounting for their movements throughout the day to assess the impact of activity-based mobility pattern on air pollution exposure. Results of PM2.5 exposures estimated statically (at residential locations) versus dynamically (over residents’ activity-based mobility) demonstrates that the two methods yield statistically significant different results (p < 0.05). In addition, the comparison conducted in different age groups shows that the difference between these two approaches is greater among youth and working age residents, whereas seniors show a similar pattern using both approaches due to their lower rates of travel activity.
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Singleton, Patrick A., Joseph C. Totten, Jaime P. Orrego-Oñate, Robert J. Schneider, and Kelly J. Clifton. "Making Strides: State of the Practice of Pedestrian Forecasting in Regional Travel Models." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 35 (May 14, 2018): 58–68. http://dx.doi.org/10.1177/0361198118773555.
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Much has changed in the 30 years since non-motorized modes were first included in regional travel demand models. As interest in understanding behavioral influences on walking and policies requiring estimates of walking activity increase, it is important to consider how pedestrian travel is modeled at a regional level. This paper evaluates the state of the practice of modeling walk trips among the largest 48 metropolitan planning organizations (MPOs) and assesses changes made over the last 5 years. By reviewing model documentation and responses to a survey of MPO modelers, this paper summarizes current practices, describes six pedestrian modeling frameworks, and identifies trends. Three-quarters (75%) of large MPOs now model non-motorized travel, and over two-thirds (69%) of those MPOs distinguish walking from bicycling; these percentages are up from nearly two-thirds (63%) and one-half (47%), respectively, in 2012. This change corresponds with an increase in the deployment of activity-based models, which offer the opportunity to enhance pedestrian modeling techniques. The biggest barrier to more sophisticated models remains a lack of travel survey data on walking behavior, yet some MPOs are starting to overcome this challenge by oversampling potential active travelers. Decision-makers are becoming more interested in analyzing walking and using estimates of walking activity that are output from models for various planning applications. As the practice continues to mature, the near future will likely see smaller-scale measures of the pedestrian environment, more detailed zonal and network structures, and possibly even an operational model of pedestrian route choice.
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Mohammadian, Abolfazl, and Eric J. Miller. "Empirical Investigation of Household Vehicle Type Choice Decisions." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (January 2003): 99–106. http://dx.doi.org/10.3141/1854-11.
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Automobile ownership models are an integral part of comprehensive transportation modeling systems. Recent work and ongoing advances in the area of activity-based travel demand modeling have recognized the need for increased experimentation with automobile choice models. On the other hand, while automobiles are very important in people's everyday lives, they also have a serious impact on the environment. This impact occurs at the micro level (pollution) as well as the macro level (emission of greenhouse gases and global warming). Such impacts have led to increased interest in reducing motor vehicle emissions. A household automobile type choice model was developed at a disaggregate level. The model can provide a direct forecast of consumer demand for personal-use vehicles given the available choices. A well-developed form of discrete choice modeling techniques, the nested logit model, was used to investigate the process of household automobile type choice decisions given that a transaction has occurred.
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Moeckel, Rolf, Leta Huntsinger, and Rick Donnelly. "From Macro to Microscopic Trip Generation: Representing Heterogeneous Travel Behavior." Open Transportation Journal 11, no. 1 (March 23, 2017): 31–43. http://dx.doi.org/10.2174/1874447801711010031.
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Background: In four-step travel demand models, average trip generation rates are traditionally applied to static household type definitions. In reality, however, trip generation is more heterogeneous with some households making no trips and other households making more than a dozen trips, even if they are of the same household type. Objective: This paper aims at improving trip-generation methods without jumping all the way to an activity-based model, which is a very costly form of modeling travel demand both in terms of development and computer processing time. Method: Two fundamental improvements in trip generation are presented in this paper. First, the definition of household types, which traditionally is based on professional judgment rather than science, is revised to optimally reflect trip generation differences between the household types. For this purpose, over 67 million definitions of household types were analyzed econometrically in a Big-Data exercise. Secondly, a microscopic trip generation module was developed that specifies trip generation individually for every household. Results: This new module allows representing the heterogeneity in trip generation found in reality, with the ability to maintain all household attributes for subsequent models. Even though the following steps in a trip-based model used in this research remained unchanged, the model was improved by using microscopic trip generation. Mode-specific constants were reduced by 9%, and the Root Mean Square Error of the assignment validation improved by 7%.
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Nurul Habib, Khandker M., Catherine Morency, and Martin Trépanier. "Integrating parking behaviour in activity-based travel demand modelling: Investigation of the relationship between parking type choice and activity scheduling process." Transportation Research Part A: Policy and Practice 46, no. 1 (January 2012): 154–66. http://dx.doi.org/10.1016/j.tra.2011.09.014.
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Doherty, Sean T., and Abolfazl Mohammadian. "Application of Artificial Neural Network Models to Activity Scheduling Time Horizon." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (January 2003): 43–49. http://dx.doi.org/10.3141/1854-05.
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Machine-learning techniques are increasingly being applied in the areas of exploratory data analysis, prediction, and classification. At the same time that analytical techniques are expanding, new conceptual approaches to the modeling of travel are emerging in an effort to improve travel demand forecasting and better assess the impacts of emerging transportation policy. In particular, the shift toward activity-based travel analysis has led to the development of activity scheduling models. One of the key features of emerging models of this type is the attempt to simulate the order in which activities are added during a continuous process of schedule construction. In practice, a fixed order by activity type is often assumed; for example, work activities are planned first, followed by the planning of more discretionary activity types. By using observed data on the scheduling process from a small sample of households from Quebec City, Quebec, Canada, a neural network model that classifies activities according to the order in which they were planned, the planning time horizon (preplanned, planned, or impulsive), was developed. A variety of explanatory variables were used in the model related to individual-, household-, and activity-based characteristics such as spatial and temporal fixities. The model developed exhibited a relatively high degree of prediction with the test data, especially for the preplanned and impulsive categories of the planning time horizon. These results suggest that machine-learning algorithms could be used to predict the order in which activities are selected in emerging activity scheduling process models, thereby avoiding static assumptions related purely to activity type.
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Hafezi, Mohammad Hesam, Lei Liu, and Hugh Millward. "Learning Daily Activity Sequences of Population Groups using Random Forest Theory." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 47 (July 31, 2018): 194–207. http://dx.doi.org/10.1177/0361198118773197.
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The choice of daily activity sequences differs between individuals based on their socio-demographic characteristics and their health and/or mobility status. The aim of this paper is to provide an improved methodology for learning and modeling the daily activity engagement patterns of individuals using a state-of-the-art machine learning algorithm. The dependencies between activity type, activity frequency, activity sequence, and socio-demographic characteristics of individuals are taken into account by employing a random forest model. In order to capture the heterogeneity and diversity among the predictor variables, we employed two different methods for split selection in the random forest algorithm: Classification and Regression Tree (CART) and curvature search. These two methods were examined under two different layer settings. In the first setting, the algorithm grows trees using all alternative predictor variables, whereas in the second setting the importance of the predictor variables is estimated and then the algorithm grows trees using only high-score predictor variables. The models were applied to time use data from the large Halifax Space-Time Activity Research (STAR) household travel diary survey. We evaluated the estimation accuracy of the proposed models using confusion matrix, transition matrix, and sequential alignment techniques. Results show that the random forest model with CART split selection using the first layer setting has the best accuracy in replicating activity agendas and activity sequences of individuals. The results of this paper are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT).
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Hasnine, Md Sami, and Khandker Nurul Habib. "Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: a review of state-of-the-art." Transport Reviews 41, no. 1 (June 19, 2020): 5–26. http://dx.doi.org/10.1080/01441647.2020.1780648.
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Walker, Joan L. "Making Household Microsimulation of Travel and Activities Accessible to Planners." Transportation Research Record: Journal of the Transportation Research Board 1931, no. 1 (January 2005): 38–48. http://dx.doi.org/10.1177/0361198105193100105.
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There is a large gap between the aggregate, trip-based models used by transportation planning agencies and the activity-based, microsimulation methods espoused by those at the forefront of research. The modeling environment presented here is intended to bridge this gap by providing a palatable way for planning agencies to move toward advanced methods. Three components to bridging the gap are emphasized: an incremental approach, a demonstration of clear gains, and a provision of an environment that eases initial implementation and allows for expansion. The modeling environment (called STEP2) is a household microsimulator, developed in TransCAD, that can be used to implement a four-step model as well as models with longer-term behavior and trip chaining. An implementation for southern Nevada is described, and comparisons are made with the region's aggregate four-step model. The models perform similarly in numerous ways. A key advantage to the microsimulator is that it provides impacts by socioeconomic group (essential for equity analysis) and individual trip movements (for use in a vehicle microsimulator). A sensitivity analysis indicates that the microsimulation model has less inelastic cross elasticity of transit demand with respect to auto travel times than the aggregate model (aggregation error). The trade-off is that microsimulators have simulation error; results are presented regarding the severity of this error. This work shows that a shift to microsimulation does not necessarily require substantial investment to achieve many of the benefits. One of the greatest advantages is a flexible environment that can expand to include additional sensitivity to demographics and transportation policy variables.
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Babu, Devika, Sreelakshmi Balan, and M. V. L. R. Anjaneyulu. "Activity-travel patterns of workers and students: a study from Calicut city, India." Archives of Transport 46, no. 2 (June 30, 2018): 21–32. http://dx.doi.org/10.5604/01.3001.0012.2100.
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Travel behaviour studies in activity-based perspective treat travel as a result of individual’s desire to participate in different activities. This approach is more significant in the context of developing countries, as the transportation problems are more severe here. Since, commuters contribute to a major share in the travel, understanding their travel behaviour is essential. This paper aims to explore the travel behaviour of commuters in Calicut city, Kerala State, India and thereby model their activity-travel patterns. Household, personal and activity-travel information from 12920 working people and 9684 students formed the database for this study. The data collection was performed by means of home-interview survey by face-to-face interview technique. From preliminary analysis, several simple and complex tours were identified for the study area. Working people’s work participation and students’ education activity participation decision are modelled as mandatory activity participation choice in a binary logit modelling framework. Results of this mandatory activity participation model revealed that male workers are more likely to engage in work compared to females. Presence of elderly persons is found to negatively influence the work participation decisions of workers. This may be due to the fact that, work activity may be partially or completely replaced with the medical requirements of the elderly. The chances for work activity participation increase with increase in number of two-wheelers at home. In the case of students, as the education level increases, they are found to be less likely to participate in education activities. Students are observed to follow simple activity-travel pattern. Complex tours are found to be performed by males, compared to females. Activity-travel pattern of the study group are predicted using the developed models. The percentages correctly predicted indicate reasonably good predictability for the models. These kind of studies are expected to help the town planners to better understand city’s travel behaviour and thus to formulate well-organised travel demand management policies.
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Edrisi, Ali, Peyman Ashkrof, and Houmaan Ganjipour. "Modelling the Effect of Information and Communication Technology on Activity-Based Travels, Case Study: Tehran." Transport and Telecommunication Journal 20, no. 4 (December 1, 2019): 346–56. http://dx.doi.org/10.2478/ttj-2019-0028.
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Abstract Information and communication technology (ICT) has revolutionized almost all aspects of human life. Thus, examining the effect of this technology on social problems such as traffic congestion, environmental pollution, urban design, land use etc. is of immense importance for transportation planning. Using 303 questionnaires completed correctly by residents of Tehran, this study applied Structural Equation Modelling to investigate the effect of ICT usage in a broader definition on 27 types of out-of-home activities, which were classified into three categories of subsistence, maintenance and leisure activities. The results showed the complementary/generation effect of ICT usage on travel demand. The use of ICT was also found to increase the time spent on subsistence activities and decrease the time spent on maintenance activities, as out-of-home maintenance activities can be carried out more easily and rapidly by Internet, and so people naturally prefer to follow this approach.
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Hilgert, Tim, Sascha von Behren, Christine Eisenmann, and Peter Vortisch. "Are Activity Patterns Stable or Variable? Analysis of Three-Year Panel Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 47 (May 14, 2018): 46–56. http://dx.doi.org/10.1177/0361198118773557.
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Routines and mandatory activities, such as work and school, shape the activity patterns of individuals and strongly influence travel demand. Knowledge about stability and variability of these routines could strengthen travel demand modelling and forecasting. A longitudinal perspective is required to investigate these aspects. In this study, the activity patterns of a sample of people is compared for one week in two successive years. It is analyzed whether the activity patterns of a given person vary from year to year, to what degree, and how this variability and stability can be measured. It is considered whether socio-demographic factors and life events determine stability in weekly activity patterns. The study is based on the representative panel survey, German Mobility Panel. The weekly activity patterns of the same respondents in different years is assessed, using two methods to measure stability and variability. The survey respondents are clustered into three groups according to the degree of variability in their activity patterns. A logistic regression model is also used to identify socio-economic and demographic covariates for similarity in weekly activity patterns. Results show that about one-third of the sample had the same or very similar weekly activity patterns in the two years examined. A person’s occupation status is a good predictor for the variability of activity patterns. Moreover, persons undergoing a change in occupation status are quite likely to show a greater variability in their activity patterns.
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Zhang, Lei, Di Yang, Sepehr Ghader, Carlos Carrion, Chenfeng Xiong, Thomas F. Rossi, Martin Milkovits, Subrat Mahapatra, and Charles Barber. "An Integrated, Validated, and Applied Activity-Based Dynamic Traffic Assignment Model for the Baltimore-Washington Region." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 51 (September 18, 2018): 45–55. http://dx.doi.org/10.1177/0361198118796397.
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The paper discusses the integration process and initial applications of a new model for the Baltimore-Washington region that integrates an activity-based travel demand model (ABM) with a dynamic traffic assignment (DTA) model. Specifically, the integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council, and DTALite, a mesoscopic DTA model. The integrated model simulates the complete daily activity choices of individuals residing in the model region, including long-term choices, such as workplace location; daily activity patterns, including joint household activities and school escorting; activity location choices; time-of-day choices; mode choices; and route choices. The paper describes the model development and integration approach, including modeling challenges, such as the need to maintain consistency between the ABM and DTA models in terms of temporal and spatial resolution, and practical implementation issues, such as managing model run time and ensuring sufficient convergence of the model. The integrated model results have been validated against observed daily traffic volumes and vehicle-miles traveled (VMT) for various functional classes. A land-use change scenario that analyzes the redevelopment of the Port Covington area in Baltimore is applied and compared with the baseline scenario. The validation and application results suggest that the integrated model outperforms a static assignment-based ABM and could capture behavioral changes at much finer time resolutions.
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Guan, Ye, Shi, and Zou. "A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China." Sustainability 11, no. 20 (October 17, 2019): 5768. http://dx.doi.org/10.3390/su11205768.
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This paper investigates the outdoor non-work activity allocation behaviors of commuters in Xiaoshan District of Hangzhou, China, as well as the underlying relationship among different types of outdoor non-work activities. As per their commute and work schedules, commuters’ outdoor non-work activities are classified into six categories and considered as binary dependent variables for modeling analysis, including from home before work, on commute way from home to work, going home during work, going out (not going home) during work, on commute way from work back home, and from home after work. Independent variables include commute attributes, work schedules, sociodemographic attributes, and built-environmental attributes. A multivariate probit model is developed to explore the effects of explanatory variables and capture correlations among unobserved influential factors. The model estimation results show that daily work time, education years, and traffic zone have substantial impacts on commuters’ non-work activity allocations. As for the underlying relationship among unobserved factors, a positive correlation is found between the outdoor non-work activities on commute way to and from work, indicating a mutually promotive relationship. All other correlations are negative, indicating other types of non-work activities are mutually substitutive. These findings will help to better understand commuters’ behaviors of outdoor activity arrangement subject to the time-space constraint from fixed work schedules, and shed some light on the mechanism of complex work tour formation, so as to guide the development of activity-based travel demand models for commuters.
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Kim, PhD, Karl, Pradip Pant, PhD, and Eric Yamashita, MURP. "Evacuation planning for plausible worst case inundation scenarios in Honolulu, Hawaii." Journal of Emergency Management 13, no. 2 (March 1, 2015): 93. http://dx.doi.org/10.5055/jem.2015.0223.
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Honolulu is susceptible to coastal flooding hazards. Like other coastal cities, Honolulu's long-term economic viability and sustainability depends on how well it can adapt to changes in the natural and built environment. While there is a disagreement over the magnitude and extent of localized impacts associated with climate change, it is widely accepted that by 2100 there will be at least a meter in sea level rise (SLR) and an increase in extreme weather events. Increased exposure and vulnerabilities associated with urbanization and location of human activities in coastal areas warrants serious consideration by planners and policy makers.This article has three objectives. First, flooding due to the combined effects of SLR and episodic hydrometeorological and geophysical events in Honolulu are investigated and the risks to the community are quantified. Second, the risks and vulnerabilities of critical infrastructure and the surface transportation system are described. Third, using the travel demand software, travel distances and travel times for evacuation from inundated areas are modeled.Data from three inundation models were used. The first model simulated storm surge from a category 4 hurricane similar to Hurricane Iniki which devastated the island of Kauai in 1992. The second model estimates inundation based on five tsunamis that struck Hawaii. A 1-m increase in sea level was included in both the hurricane storm surge and tsunami flooding models. The third model used in this article generated a 500-year flood event due to riverine flooding. Using a uniform grid cell structure, the three inundation maps were used to assess the worst case flooding scenario. Based on the flood depths, the ruling hazard (hurricane, tsunami, or riverine flooding) for each grid cell was determined. The hazard layer was analyzed with socioeconomic data layers to determine the impact on vulnerable populations, economic activity, and critical infrastructure. The analysis focused both on evacuation needs and the critical elements of the infrastructure system that are needed to ensure effective response and recovery in the advent of flooding.This study shows that the coastal flooding will seriously affect the economy and employment. Extreme flooding events could affect 38 percent of the freeways, 44 percent of the highways, 69 percent of the arterial roads, and 40 percent of the local streets in the area examined. Approximately 80 percent of the economy and 76 percent of the total employment in the urban core of Honolulu is exposed to flooding. Evacuation modeling, shelter accessibility, and travel time to shelter analyses revealed that there is a significant shortage in sheltering options, as well as increases in travel times and distances as inundation depth increases. The findings are useful for evacuation and shelter planning for extreme coastal events, as well as for climate change adaptation planning in Honolulu. Recommendations for emergency responders as well as those interested in the integration of long-term SLR and low probability, high consequence coastal hazards are included. The study shows how to integrate travel demand modeling across multiple hazards and threats related to evacuating, sheltering, and disaster risk reduction.
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Zhang, Junyi, Harry Timmermans, and Aloys Borgers. "Utility-Maximizing Model of Household Time Use for Independent, Shared, and Allocated Activities Incorporating Group Decision Mechanisms." Transportation Research Record: Journal of the Transportation Research Board 1807, no. 1 (January 2002): 1–8. http://dx.doi.org/10.3141/1807-01.
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Existing activity-based models of transport demand typically assume an individual decision-making process. The focus on theories of individual decision making may be partially due to the lack of behaviorally oriented modeling methodologies for group decision making. Therefore, an attempt has been made to develop a new model (called the g-Logit household time-use model) for time-use analysis that incorporates group decision-making mechanisms. To do that, it is proposed that household utility function be defined in the form of multilinear utility function, which can represent interactions among household members and interactions among their activities (four types of activity: in-home, out-of-home independent, allocated, and shared). By introducing this household utility function into the time allocation approach, each member’s time-use functions for different types of activities are obtained. The function for independent activities has a structure similar to the one for allocated activities, except the weight parameters are different. In contrast, the time-use function for shared activities has a completely different structure, which results from the complicated processes and strategies for household decision making. The effectiveness of the proposed model is confirmed with activity-travel diary data.
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Elessa Etuman, Arthur, and Isabelle Coll. "OLYMPUS v1.0: development of an integrated air pollutant and GHG urban emissions model – methodology and calibration over greater Paris." Geoscientific Model Development 11, no. 12 (December 12, 2018): 5085–111. http://dx.doi.org/10.5194/gmd-11-5085-2018.
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Abstract. Air pollutants and greenhouse gases have many effects on health, the economy, urban climate and atmospheric environment. At the city level, the transport and heating sectors contribute significantly to air pollution. In order to quantify the impact of urban policies on anthropogenic air pollutants, the main processes leading to emissions need to be understood: they principally include mobility for work and leisure as well as household behavior, themselves impacted by a variety of social parameters. In this context, the OLYMPUS modeling platform has been designed for environmental decision support. It generates a synthetic population of individuals and defines the mobility of each individual in the city through an activity-based approach of travel demand. The model then spatializes road traffic by taking into account congestion on the road network. It also includes a module that estimates the energy demand of the territory by calculating the unit energy consumption of households and the commercial–institutional sector. Finally, the emissions associated with all the modeled activities are calculated using the COPERT emission factors for traffic and the European Environmental Agency (EEA) methodology for heating-related combustion. The comparison of emissions with AIRPARIF's regional inventory shows discrepancies that are consistent with differences in assumptions and input data, mainly in the sense of underestimation. The methodological choices and the potential ways of improvement, including the refinement of traffic congestion modeling and of the transport of goods, are discussed.
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Yurchenko, N. I. "The Current Trends in Marketing Research in the Tourism Industry." Business Inform 10, no. 513 (2020): 450–59. http://dx.doi.org/10.32983/2222-4459-2020-10-450-459.
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Currently, the tourism industry continues to outpace the global economy despite deteriorating global economic prospects, tensions in international trade, social worries, geopolitical uncertainty, instability and the COVID-19 pandemic. The article is aimed at identifying modern trends of marketing research as part of the complex of marketing instruments in the tourism sphere. To achieve this aim, the article uses the following research methods: abstract-logical; situational analysis; mean, absolute and relative values; comparison, graphic, sociological; statistical analysis; economic-mathematical; expert surveys and estimations. Based on the data of the World Tourism Organization, the indicators of development of the world market of tourist services are analyzed. Performed were the following: analysis of the dynamics of the number of subjects of tourism activity (tour operators and travel agents) in Ukraine; total average number of full-time employees; income from the provision of tourist services; operating expenses for the provision of tourist services; number of tourists served by tour operators and travel agents in Ukraine. The content of marketing research is disclosed as a multi-stage process, which should include the collection, registration and analysis of data in the sphere of tourism business. Marketing researches should be conducted according to 8 stages: determining the problem; development of the concept of research; cabinet marketing research; field market research; analysis of market conditions (supply and demand); research of foreign markets; simulation modeling; formation of a marketing information system. In order to determine the rating of tour operators of the mass segment of the tourism market in 2020, a questionnaire containing 16 questions is specified. Its results can be used when evaluating tour operators in terms of customer comfort and cooperation with travel agents. It is proved that marketing research in the tourism industry is advisable to be carried out systematically. This will provide for substantiating and elaborating managerial solutions in order to maximize the satisfaction of the needs of consumers of tourist services and solve the problems of significant seasonal fluctuations in demand.