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In this paper, a sequential decomposition method to model the travel behavior of individuals in a mixed transportation network (transit and roads) is proposed. A mixed binary model is developed assuming the mode choice of the individuals depend on the accessibility level for different modes and parking fees of the vehicles at different locations, where the parking fees vary depending on the demand level and time of day. The efficiency of the proposed model is tested using simulated data. It is shown that by slicing the activity patterns and parallelizing some segments of the algorithm, the computation time increases linearly as the population size increases.
Mahdieh Allahviranloo. A Sequential Decomposition Approach and Integrating the Concepts for Super-Networks in the Activity-Based Models. Advances in Intelligent Systems and Computing 2021, 86 -98.
AMA StyleMahdieh Allahviranloo. A Sequential Decomposition Approach and Integrating the Concepts for Super-Networks in the Activity-Based Models. Advances in Intelligent Systems and Computing. 2021; ():86-98.
Chicago/Turabian StyleMahdieh Allahviranloo. 2021. "A Sequential Decomposition Approach and Integrating the Concepts for Super-Networks in the Activity-Based Models." Advances in Intelligent Systems and Computing , no. : 86-98.
Understanding the temporal and spatial variation of air quality (AQ) impact due to congestion pricing is important since the health and economic benefits of air quality improvements depend on the distribution of traffic-related air pollution. Aiming to improve our knowledge of the AQ impacts from congestion pricing, this study integrates a disaggregate agent-based travel demand model with a hyper-local air quality model to examine emissions, air quality, and exposure. Studying congestion pricing schemes in NYC, we find that daily single-occupancy-vehicle trips to the charging area decreases by 14.5% and 24.3% under the low and high charging schemes, respectively. Correspondingly, the PM2.5 concentration decreases by 5–25% in the Central Manhattan areas in the low-toll scenario, and by more than 10% across almost all of New York City areas in the high-toll scenario. Our results indicate non-linear relations between the adaptation of travel behavior and the resulting air quality/exposure impacts.
Mohammad Tayarani; Amirhossein Baghestani; Mahdieh Allahviranloo; H. Oliver Gao. Spatial/temporal variability in transportation emissions and air quality in NYC cordon pricing. Transportation Research Part D: Transport and Environment 2020, 89, 102620 .
AMA StyleMohammad Tayarani, Amirhossein Baghestani, Mahdieh Allahviranloo, H. Oliver Gao. Spatial/temporal variability in transportation emissions and air quality in NYC cordon pricing. Transportation Research Part D: Transport and Environment. 2020; 89 ():102620.
Chicago/Turabian StyleMohammad Tayarani; Amirhossein Baghestani; Mahdieh Allahviranloo; H. Oliver Gao. 2020. "Spatial/temporal variability in transportation emissions and air quality in NYC cordon pricing." Transportation Research Part D: Transport and Environment 89, no. : 102620.
Traffic congestion is a major challenge in metropolitan areas due to economic and negative health impacts. Several strategies have been tested all around the globe to relieve traffic congestion and minimize transportation externalities. Congestion pricing is among the most cited strategies with the potential to manage the travel demand. This study aims to investigate potential travel behavior changes in response to cordon pricing in Manhattan, New York. Several pricing schemes with variable cordon charging fees are designed and examined using an activity-based microsimulation travel demand model. The findings demonstrate a decreasing trend in the total number of trips interacting with the central business district (CBD) as the price goes up, except for intrazonal trips. We also analyze a set of other performance measures, such as Vehicle-Hours of Delay, Vehicle-Miles Traveled, and vehicle emissions. While the results show considerable growth in transit ridership (6%), single-occupant vehicles and taxis trips destined to the CBD reduced by 30% and 40%, respectively, under the $20 pricing scheme. The aggregated value of delay for all vehicles was also reduced by 32%. Our findings suggest that cordon pricing can positively ameliorate transportation network performance and consequently, improve air quality by reducing particular matter inventory by up to 17.5%. The results might facilitate public acceptance of cordon pricing strategies for the case study of NYC. More broadly, this study provides a robust framework for decision-makers across the US for further analysis on the subject.
Amirhossein Baghestani; Mohammad Tayarani; Mahdieh Allahviranloo; H. Oliver Gao. Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City. Sustainability 2020, 12, 3655 .
AMA StyleAmirhossein Baghestani, Mohammad Tayarani, Mahdieh Allahviranloo, H. Oliver Gao. Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City. Sustainability. 2020; 12 (9):3655.
Chicago/Turabian StyleAmirhossein Baghestani; Mohammad Tayarani; Mahdieh Allahviranloo; H. Oliver Gao. 2020. "Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City." Sustainability 12, no. 9: 3655.
Historically cities are formed to provide interaction and communication opportunities for communities. As cities become smarter, new forms of interactions are formed and the necessity to participate in activities such as traveling to a grocery store is replaced by submission of online order in Amazon fresh. If we move in this direction, it bears answering the question of what kinds of societal loss, or changes in social interactions should we expect in our future cities? In this paper, we develop the Shared Life Experience (SLE) metric, focusing on the interaction opportunities between people. We define this metric to be measured based on the pairwise reachability and interaction probabilities of city dwellers in the context of time and space. Furthermore, we present a framework discussing how this metric can be embedded into the design of a more dynamic urban form and how we can measure it using publicly available data. Two sets of analyses are presented. First: a bi-level model is proposed, composed of a heuristic search algorithm in the upper level to estimate the regional SLE value for a given set of parameters and finding the optimum solution. The lower level models in the bi-level structure are activity-based models producing mobility behavior of individuals in response to changes in the input parameters. Second: we present a simple methodology and discuss how to quantify the SLE index using household travel survey data collected within five boroughs of New York City. This analysis can highlight many equity-related objectives and be used as an informative tool for better decision making.
Mahdieh Allahviranloo; Thomas Bonet; Jérémy Diez. Introducing shared life experience metric in urban planning. Transportation 2020, 1 -24.
AMA StyleMahdieh Allahviranloo, Thomas Bonet, Jérémy Diez. Introducing shared life experience metric in urban planning. Transportation. 2020; ():1-24.
Chicago/Turabian StyleMahdieh Allahviranloo; Thomas Bonet; Jérémy Diez. 2020. "Introducing shared life experience metric in urban planning." Transportation , no. : 1-24.
The introduction of area-based pricing schemes is often motivated by both urban congestion and pollution concerns. Existing discrete network optimization models for the design of area pricing schemes focus primarily on travel-related objectives, such as maximizing social welfare measures based on travel costs. In this paper, an area pricing problem is proposed that explicitly accounts for both travel- and environmentally-oriented objectives to optimally define charging boundaries and tolling levels. The environmental objective is formulated from an equity perspective. Specifically, it is assumed that regional planners are interested in minimizing inequality in the levels of pollutant encountered by individuals as they perform their daily activities. Here, pollutant exposure is specified in terms of agent-level intake of pollutants. In addition, it is assumed that pricing schemes must reduce pollutant concentrations in the region below an established threshold. A network-based activity model is presented as an approach for modeling the changes in travelers’ mobility behavior and activity patterns in response to pricing schemes. A surrogate-based optimization approach is proposed to solve the area pricing problem, as it is likely that, in practice, this design problem would be computationally costly. The proposed algorithm uses a geometric representation of the charging boundary. New procedures for generating candidate boundary locations are presented, which include the use of surrogate-based methods to screen for feasible, non-dominated solutions prior to their evaluation via the computationally expensive models. The proposed model and solution heuristic are tested using the Chicago Sketch Network and a smaller test network.
Daniel Rodriguez-Roman; Mahdieh Allahviranloo. Designing area pricing schemes to minimize travel disutility and exposure to pollutants. Transportation Research Part D: Transport and Environment 2019, 76, 236 -254.
AMA StyleDaniel Rodriguez-Roman, Mahdieh Allahviranloo. Designing area pricing schemes to minimize travel disutility and exposure to pollutants. Transportation Research Part D: Transport and Environment. 2019; 76 ():236-254.
Chicago/Turabian StyleDaniel Rodriguez-Roman; Mahdieh Allahviranloo. 2019. "Designing area pricing schemes to minimize travel disutility and exposure to pollutants." Transportation Research Part D: Transport and Environment 76, no. : 236-254.
A better understanding of the connection of urban forms and travel behavior is critical to the operation of the existing and the design of the future transportation infrastructure. A comprehensive analysis of travel behavior across different regions would capture the underlying dependency among time-use behavior, the built environment, and the demographics of travelers. This paper introduces a method to measure the similarity of activity chains in different regions based on pattern segmentation and recognition. Travel behavior of residents of five different urban regions in the United States are compared: New York City, Los Angeles County, Chicago, San Francisco, and Atlanta. A total of 80,894 activity patterns is analyzed to address three goals: (1) to find a set of representative activity patterns for the residents of each region; (2) to analyze the dissimilarities/similarities in the activity patterns within and between the regions; and (3) to develop econometric models to assess and compare the role of demographic attributes on time use behavior. The outcome of the analysis supports and highlights the differences between study areas.
Mahdieh Allahviranloo; Leila Aissaoui. A comparison of time-use behavior in metropolitan areas using pattern recognition techniques. Transportation Research Part A: Policy and Practice 2019, 129, 271 -287.
AMA StyleMahdieh Allahviranloo, Leila Aissaoui. A comparison of time-use behavior in metropolitan areas using pattern recognition techniques. Transportation Research Part A: Policy and Practice. 2019; 129 ():271-287.
Chicago/Turabian StyleMahdieh Allahviranloo; Leila Aissaoui. 2019. "A comparison of time-use behavior in metropolitan areas using pattern recognition techniques." Transportation Research Part A: Policy and Practice 129, no. : 271-287.
Using a dynamic optimization model, we study the transaction of pickup/delivery activities between two groups of individuals: carriers and requesters, in a P2P crowdshipping model. Based on their value of time, requesters set maximum willingness to pay for their parcels to be picked up and delivered and carriers, make an offer depending on the changes that needs to be made to their original itinerary. The proposed model was tested for Los Angeles and Orange Counties, with 27% of successful matches, also demonstrating the impacts of crowdshipping on regional travel behavior and shifts in space-time distribution of the demand.
Mahdieh Allahviranloo; Amirhossein Baghestani. A dynamic crowdshipping model and daily travel behavior. Transportation Research Part E: Logistics and Transportation Review 2019, 128, 175 -190.
AMA StyleMahdieh Allahviranloo, Amirhossein Baghestani. A dynamic crowdshipping model and daily travel behavior. Transportation Research Part E: Logistics and Transportation Review. 2019; 128 ():175-190.
Chicago/Turabian StyleMahdieh Allahviranloo; Amirhossein Baghestani. 2019. "A dynamic crowdshipping model and daily travel behavior." Transportation Research Part E: Logistics and Transportation Review 128, no. : 175-190.
The Northeast United States, particularly New York State has experienced an increase in extreme daily precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy, have revealed vulnerability to the intense precipitation within the transportation sector. In the scale of New York City, where transit system is the most dominant mode of transportation and daily mobility of millions of passengers depends on it, any disruption in the transit service would result in gridlocks and massive delays. To assess the impacts of rainfall on the subway ridership, we merged high resolution radar rainfall and subway ridership data to conduct a detailed analysis for each of the 116 subway stations at the borough of Manhattan. The analysis is carried out on both hourly and daily resolution level, where a spatial-temporal Bayesian multi-level regression model is used to capture the underlying dependency between the parameters. The estimation results are obtained through Markov Chain Monte Carlo sampling method. The results for daily analysis indicate that during weekdays, transit ridership in the stations located in commercial zones are less sensitive to the rainfall compared to the ones in residential zones.
Shirin Najafabadi; Ali Hamidi; Mahdieh Allahviranloo; Naresh Devineni. Does demand for subway ridership in Manhattan depend on the rainfall events? Transport Policy 2018, 74, 201 -213.
AMA StyleShirin Najafabadi, Ali Hamidi, Mahdieh Allahviranloo, Naresh Devineni. Does demand for subway ridership in Manhattan depend on the rainfall events? Transport Policy. 2018; 74 ():201-213.
Chicago/Turabian StyleShirin Najafabadi; Ali Hamidi; Mahdieh Allahviranloo; Naresh Devineni. 2018. "Does demand for subway ridership in Manhattan depend on the rainfall events?" Transport Policy 74, no. : 201-213.
We study an autonomous transport service for population where users buy future time slots in which they are guaranteed service. A bilevel fleet sizing-vehicle routing-time slot pricing model, sensitive to users’ activity scheduling decisions in the lower level is developed. Upper level model is solved using Bender’s decomposition and the results are sent to lower level finding an equilibrium using the values of willingness to pay by population under different pricing mechanisms. The values of willingness to pay and the reservation of vehicles among users depends on the fleet size and routing/scheduling results obtained from the upper level model, where spatial temporal distribution of the demand for ride by users impacts the solution to fleet sizing problem. Numerical models are used to explain the methods, to test scalability of the proposed solution algorithms, and to illustrate the potential application of the proposed formulation in simultaneous assessment and modeling of population behavior and optimum fleet sizing model.
Mahdieh Allahviranloo; Joseph Y.J. Chow. A fractionally owned autonomous vehicle fleet sizing problem with time slot demand substitution effects. Transportation Research Part C: Emerging Technologies 2018, 98, 37 -53.
AMA StyleMahdieh Allahviranloo, Joseph Y.J. Chow. A fractionally owned autonomous vehicle fleet sizing problem with time slot demand substitution effects. Transportation Research Part C: Emerging Technologies. 2018; 98 ():37-53.
Chicago/Turabian StyleMahdieh Allahviranloo; Joseph Y.J. Chow. 2018. "A fractionally owned autonomous vehicle fleet sizing problem with time slot demand substitution effects." Transportation Research Part C: Emerging Technologies 98, no. : 37-53.
Taxis constitute an important component of the public transportation infrastructure in large metropolitan areas. However, when seen within a supply and demand framework the operation of taxi transportation system is far away from its optimal equilibrium, yielding a missed cost of opportunity for customers, drivers, and city planners. The key for optimizing its market lies in forecasting taxi demand with high geospatial–temporal precision. In this paper taxi pickup pattern is predicted by utilizing a deep learning approach that leverages long short-term memory (LSTM) neural networks. This study is based on publicly available taxi data for the New York City. Pickup data is binned based on geospatial and temporal informational tags, which are then clustered using principal component analysis. The spatiotemporal distribution of the taxi pickup demand is studied within short-term periods (next one hour) as well as long-term periods (next 48 hours) within each cluster. The performance and robustness of the proposed technique is evaluated through a comparison with adaptive boosting and decision tree regression models fitted to the same dataset. Numerical results show the dominance of the LSTM model on the short-term horizon and relatively smaller errors for the long-term prediction.
Shirin Najafabadi; Mahdieh Allahviranloo. Inference of Pattern Variation of Taxi Ridership Using Deep Learning Methods: A Case Study of New York City. International Conference on Transportation and Development 2018 2018, 1 .
AMA StyleShirin Najafabadi, Mahdieh Allahviranloo. Inference of Pattern Variation of Taxi Ridership Using Deep Learning Methods: A Case Study of New York City. International Conference on Transportation and Development 2018. 2018; ():1.
Chicago/Turabian StyleShirin Najafabadi; Mahdieh Allahviranloo. 2018. "Inference of Pattern Variation of Taxi Ridership Using Deep Learning Methods: A Case Study of New York City." International Conference on Transportation and Development 2018 , no. : 1.
The objective of this paper is to develop an analytical model to analyze the mobility behavior of a target population and to minimize the disparities. The analysis is conducted using household travel survey data collected from 7993 residents of five boroughs of New York City, where citizens with mobility impairments are considered as our target population. The study starts by quantifying the existing gap in the patterns of the mobility-impaired individuals and the base population followed by the generation of new activities in the agenda of the target population using the patterns of their nearest neighbors in the base population. Activities are generated based on copula sampling method. Attributes of activities forming the new agenda of the target population are the inputs that will be used for the design of an optimum ridesharing system. By integrating probabilistic models with the existing routing models, we develop a methodology to identify the optimum fleet size, optimum route, and optimum schedule such that all requests of the physically impaired travelers are fulfilled within a reasonable wait time for ride requesters.
Marouane Zellou; Mahdieh Allahviranloo. Probabilistic Fleet Sizing and Routing Problem to Minimize Mobility Disparities. Transportation Research Record: Journal of the Transportation Research Board 2018, 2672, 639 -648.
AMA StyleMarouane Zellou, Mahdieh Allahviranloo. Probabilistic Fleet Sizing and Routing Problem to Minimize Mobility Disparities. Transportation Research Record: Journal of the Transportation Research Board. 2018; 2672 (8):639-648.
Chicago/Turabian StyleMarouane Zellou; Mahdieh Allahviranloo. 2018. "Probabilistic Fleet Sizing and Routing Problem to Minimize Mobility Disparities." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 8: 639-648.
The choice of ‘dining out with friends’ or ‘wrapping up unfinished tasks at work’ depends on the utility/satisfaction gained from performing each activity while being constrained by time and physical resources. In fact, such parameters as ‘type’, ‘time of day’, ‘duration’, ‘location’, ‘companionship’, and etc. are defining factors in quantifying the utility of activities - a challenging problem which has been the focus of research for many years. This paper proposes a methodology to estimate the parameters of utility distributions for joint and solo activities, along with the penalty values associated with the deviation of activity start time and duration from their modal values. The study utilizes travel survey data collected in Frauenfeld, Switzerland, over the period of six weeks in 2003. The proposed model is a bi-level optimization model, where the upper level maximizes the accuracy of the activity scheduling on the aggregate level and is measured using the outputs of lower level optimization models. Each lower level model is a variation of pickup and delivery problem and schedules activities for each individual in the population using the parameters of utility distribution and penalty values generated by the Genetic Algorithm. The results indicate that travelers are trying to be more consistent with their arrival time to work, school and pickup/drop off activities: the associated penalty values for deviation from the modal value for arrival time to work and school activities are high. Additionally, significant differences in the parameters of the estimated utility distribution for joint and solo activities are observed, reflecting the fact that utility gained from joint and solo activities are different and needs more in-depth investigation. The proposed methodology has the potential to be applied to any multiday travel survey data, which due to advances made in handheld smart devices and mobile applications are becoming more convenient to collect.
Mahdieh Allahviranloo; Kay Axhausen. An optimization model to measure utility of joint and solo activities. Transportation Research Part B: Methodological 2018, 108, 172 -187.
AMA StyleMahdieh Allahviranloo, Kay Axhausen. An optimization model to measure utility of joint and solo activities. Transportation Research Part B: Methodological. 2018; 108 ():172-187.
Chicago/Turabian StyleMahdieh Allahviranloo; Kay Axhausen. 2018. "An optimization model to measure utility of joint and solo activities." Transportation Research Part B: Methodological 108, no. : 172-187.
Chains of activities performed during the course of the day are interconnected such that participation in one activity and the time allocated to that specific activity correspondingly influence the time-use behavior of a traveler along the course of the day. This points to the importance of analyzing trajectories of patterns as a set of activities with such specific characteristics as start time, duration, and sequence, rather than simply analyzing participation in each activity singularly. In this paper we present a methodology to answer a main question in the trajectory analysis: How to generate activity patterns trajectories, and how to conduct useful analysis that eventually makes inferences drawn from the time-use behavior of individuals applicable to the population-at-large possible? The methodology presented in this paper can be applied to synthetize chains of activities and their space–time distribution. It starts with clustering the activity patterns into a small set of representative patterns by using message passing algorithms, and then capturing the correlation among demographic profiles of travelers to the bundles of activities performed and their corresponding time sequence using multivariate probit models. We apply the methodology to two sets of data: (1) California household travel survey data for year 2000–2001, and (2) California household travel survey data for year 2010–2011. The longitudinal analysis performed in this work: (1) proves the robustness of proposed methodology in replicating time-use behavior and synthetizing activity chains, (2) reveals dynamics of changes in the trajectories of activity patterns during a 10-year time span, and (3) quantifies the influence of different socio-demographic variables on the trajectories of activities performed by travelers by implementing a statistical analysis on the distribution of estimates.
Mahdieh Allahviranloo; Robert Regue; Will Recker. Modeling the activity profiles of a population. Transportmetrica B: Transport Dynamics 2016, 5, 426 -449.
AMA StyleMahdieh Allahviranloo, Robert Regue, Will Recker. Modeling the activity profiles of a population. Transportmetrica B: Transport Dynamics. 2016; 5 (4):426-449.
Chicago/Turabian StyleMahdieh Allahviranloo; Robert Regue; Will Recker. 2016. "Modeling the activity profiles of a population." Transportmetrica B: Transport Dynamics 5, no. 4: 426-449.
This paper focuses on the development of a methodology to identify the latent factors leading to changes in the planned itineraries of travellers that result in their actual activity patterns. Specifically, we propose a way to utilise patterns of activities established by individuals across multiple days to generate possible alternative actions by these individuals when faced with conditions that produce a discrepancy between performed and planned patterns on a particular day. The choice alternatives, which are unobserved, are inferred by rules applied to comprehensive multiday data collected in Belgium, consisting of information regarding planned activity itineraries, performed activity/travel diaries, and demographics of travellers. These data are utilised to analyse and explore the underlying reasons preventing individuals from performing their planned activities on a given day, and to identify the influential parameters that lead individuals to trade their planned patterns with those actually performed. Using multiday data, we generate all possible combinations of categories of activities – mandatory, maintenance, discretionary, and pickup/drop off activities – that can form patterns for individuals. Under the assumption that the performed patterns have the closest utility to the planned patterns, we estimate the latent factors that influence travellers’ time use behaviour using a multinomial probit choice structure in which the covariance structure of the choice alternatives is specified in terms of the overlap in activities. We further identify the ‘costs’ associated with making changes in planned agenda (replacing, inserting, or deleting an activity). These penalty values are estimated using ‘Parallel Genetic Algorithm’, where the fitness function is the likelihood function estimated under the multinomial choice model structure. The results show that individuals’ mobility decisions related to mandatory activities are more robust than those associated with their non-mandatory counterparts.
Mahdieh Allahviranloo; Will Recker; Harry J.P. Timmermans. Trade-offs among planned versus performed activity patterns. Transportmetrica B: Transport Dynamics 2016, 5, 342 -363.
AMA StyleMahdieh Allahviranloo, Will Recker, Harry J.P. Timmermans. Trade-offs among planned versus performed activity patterns. Transportmetrica B: Transport Dynamics. 2016; 5 (3):342-363.
Chicago/Turabian StyleMahdieh Allahviranloo; Will Recker; Harry J.P. Timmermans. 2016. "Trade-offs among planned versus performed activity patterns." Transportmetrica B: Transport Dynamics 5, no. 3: 342-363.
Understanding scheduling behavior of households has been the focus of research for nearly half a century. Presumably activity engagement is being impacted by the importance of the activity to household members as well as time and cost constraints. Depending on the level of time budget, household members would eliminates some activities from the agenda or replace them with higher priority ones. In this paper, in order to capture the importance of different activities, we propose a methodology to schedule household activities under different levels of uncertainty about the importance of the activity. In this approach we combine discrete choice models and concepts of Fuzzy logic to identify core versus non-core activities in the agenda. The possibility of inclusion of an activity is the agenda is computed by estimating the expected importance of the activity and mapping to a set of fuzzy graphs. Activity scheduling and selection is then modeled as the outcome of a mixed integer optimization problem, in which the objective function is maximizing the expected desirability gained from activities and total saved time, subject to network connectivity, time windows, time budget and cost budget constraints.
Mahdieh Allahviranloo; Will Recker. Identifying Core vs. Non-Core Activities of Household Members. Journal of Fuzzy Set Valued Analysis 2016, 2016, 28 -53.
AMA StyleMahdieh Allahviranloo, Will Recker. Identifying Core vs. Non-Core Activities of Household Members. Journal of Fuzzy Set Valued Analysis. 2016; 2016 (1):28-53.
Chicago/Turabian StyleMahdieh Allahviranloo; Will Recker. 2016. "Identifying Core vs. Non-Core Activities of Household Members." Journal of Fuzzy Set Valued Analysis 2016, no. 1: 28-53.
GPS enabled devices, generating high-resolution spatial–temporal data, are opening new lines of possibilities for transportation applications in both planning and research. Mining these rich and large datasets to infer people’s travel behavior, the activity patterns resulting from their behavior, and allocating activities in the network is the focus of this paper. Here we introduce a methodology that relies only on geocoded location data and socioeconomic characteristics to infer types of activities in which individuals engage at different locations in the network. Depending on the duration of the stop, arrival time and geographic distance to home location and previous activities, the type of activity is inferred at the census tract level using adaptive boosting algorithm. Then, using a model based on Markov chains with conditional random field to capture dependency between activity sequencing and individuals’ socioeconomic attributes, the spatial–temporal trajectory of activity/travel engagement is generated. The model is trained on data obtained from the California Household Travel Survey data 2000–2001 and subsequently applied to an out-of sample test set to validate the accuracy and performance.
Mahdieh Allahviranloo; Will Recker. Mining activity pattern trajectories and allocating activities in the network. Transportation 2015, 42, 561 -579.
AMA StyleMahdieh Allahviranloo, Will Recker. Mining activity pattern trajectories and allocating activities in the network. Transportation. 2015; 42 (4):561-579.
Chicago/Turabian StyleMahdieh Allahviranloo; Will Recker. 2015. "Mining activity pattern trajectories and allocating activities in the network." Transportation 42, no. 4: 561-579.