Number Of Daily Trips Research Articles

Exploring transportation equity issues for persons with disabilities: The impact of gender on mobility and accessibility indicators

IntroductionPersons with disabilities still face numerous barriers in their everyday lives, especially in transportation. The problem of access to transportation is identified through fewer daily trips and more barriers in public transport. These problems indicate transportation equity issues among persons with disabilities. Another issue recognized in previous studies was a gender imbalance in the samples that could indicate gender equality problems among persons with disabilities. This paper aims to investigate mobility and accessibility indicators among persons with disabilities, with a particular emphasis on the impact of gender, in order to explore two critical transportation equity issues: inclusivity and social justice. MethodsThe mobility and accessibility indicators analyzed in this research were the Number of daily trips, Mobility satisfaction, Infrastructure accessibility satisfaction, and Modal distributions of daily trips. The data about indicators was collected by an online version of a questionnaire. The research was performed on 100 persons with disabilities from Serbia, equally distributed by gender. The most common recorded diagnosis of the respondents was spinal cord injury. ResultsIn general, respondents made 3.26 trips per day. At two transportation modes, we identified significant differences between males and females. Males frequently tripped as passenger car drivers, and females often traveled as passengers in passenger cars. Additionally, nine Bayesian linear regression models with weak informative prior distributions brought much helpful information. ConclusionsThe obtained results bring stakeholders valuable information in creating policies, practices, and recommendations to achieve specific goals related to transportation equity. Improving flexibility and time efficiency of females’ trips and resolving the problem of females’ fear of abuse and harassment in taxi cars were recognized as the two most critical topics.

Exploring the endogenous effects among car dependency, work arrangement choice, and daily travel using the 2017 NHTS data

The paper presents an effort in investigating the cause-effect relationships among telecommuting, car dependency, and trip making behavior. Data from the 2017 National Household Travel Survey (NHTS) was used to develop a structural equations model (with latent constructs) to examine the endogenous effects between the decision to telework, the degree of car dependency, and the number of daily trips. Using confirmatory factor analysis, car dependency was measured by vehicle ownership, annual mileage, and car use frequency. The model provided interesting insights. Car dependency had a negative (both direct and total) impact on telework, an inference that confirms the role of car dependency as a resistant factor against alternative work arrangements and potentially any other new tech-based policies. In the opposite direction and surprisingly, telework encouraged car dependency, which might indicate that teleworkers tended to utilize the relaxed temporal-spatial constraints for non-work trips, and they were unlikely to give up private vehicle ownership simply because of their option to telecommute. Second, number of daily trips tended to increase the probability to telework, confirming the positive endogenous role of daily activity patterns on work arrangement decisions in a daily framework. Lastly, the significant negative impact of telework on daily trips was positively mediated through car dependency, leading to a smaller negative total impact on daily trips. Our study confirms that the association between telework and trip generation is quite a complex and multi-faceted relationship, leaving the argument regarding whether or not telework increases the number of daily trips as a debatable topic that calls for further exploration.

Exploring the Potentials of Open-Source Big Data and Machine Learning in Shared Mobility Fleet Utilization Prediction

The urban transportation landscape has been rapidly growing and dynamically changing in recent years, supported by the advancement of information and communication technologies (ICT). One of the new mobility trends supported by ICT is shared mobility, which has a positive potential to reduce car use externalities. These systems’ recent and sudden introduction was not adequately planned for, and their rapidly growing popularity was not expected, which resulted in the urgent need for different stakeholders’ intervention to ensure efficient services’ integration within the urban transportation networks and to grant an effective system operation. Several challenges face shared mobility, including fleet size management, vehicle distribution, demand balancing, and the definition of equitable prices. In this research, we developed a practical, straightforward methodology that utilizes big open-source data and different machine learning (ML) algorithms to predict the daily shared-e-scooter fleet utilization (the daily number of trips per vehicle) that could be used to drive the system’s operation policies. We used four ML algorithms with different levels of complexity, namely; Linear Regression, Support Vector Regression, Gradient Boosting Machine, and Long Short-Term Memory Neural Network, to predict the fleet utilization in Louisville, Kentucky, using the knowledge the models get from the training data in Austin, Texas. The Gradient Boosting Machine (LightGBM) was the model with the best performance prediction based on the different evaluation measures. The most critical factors impacting daily fleet utilization prediction were temporal time series features, sociodemographics, meteorological data, and the built environment.

Study on facility location of air taxi skyports using a prescriptive analytics approach

The current study identifies infrastructure locations for air taxi operations in New York City (NYC) using a prescriptive analytics approach. Based on the potential air taxi demand estimated by prior studies, 14 unique sites are identified by an unsupervised machine learning technique called clustering large applications (CLARA) in Step-1. However, developing all the suggested stations simultaneously might be challenging for any business, and therefore, an integer programming model is developed in Step-2 to propose opening these centers in multiple phases, considering parameters such as rental cost, daily number of trips, easy access to road facilities, and employee salary, while maximizing the demand satisfaction. Sensitivity analysis is then conducted to identify the impact of various market penetration strategies (such as aggressive, defensive and balanced), inflation in the rental cost and employee salary, and the emergence of latent demand on the phased opening of the facilities. The clustering algorithm results indicate seven air taxi facilities to be established in the Manhattan borough, four in Queens, two in Brooklyn, and one in New Jersey, spread across three phases. Larger vertiports are suggested for airports, such as John F Kennedy and LaGuardia international airports, since they serve approximately 55% of the overall demand. Results also showed that varying percentages of the budgeted rental cost and employee salary have minimal effect on the total number of stations.

Optimal Sequential Distribution Planning for Low-Voltage Network With Electric Vehicle Loads

There has been a growing presence of electric vehicles in many countries including Thailand, where many forms of incentives have been provided to build integrated infrastructure, and to encourage drivers to switch to electric vehicles (EVs). Because the immediate entry of EVs unavoidably can alter household load profiles, reinforcement on the existing system based on traditional planning may not be sufficient and can introduce over or under capital and operating expenditure over the time horizon. Therefore, if distribution systems are unreadily prepared for such an uptake, three obvious problems can be expected: 1) voltage regulation, 2) overloads of the distribution feeders and the distribution transformers, and 3) high energy loss. In this paper, an activity-based, time-sequential Monte Carlo Simulation algorithm was comprehensively developed for uncontrollable and smart charging, given annually updated information of EV locations and number of EVs, their energy consumption, hourly average vehicle speed, number of daily trips, travel distance per trip, size of EV batteries, time to arrive home and time to leave home. Minimizing the annual sum of investment and operating costs over a planning period could then be sequentially solved by a Particle Swarm Optimization (PSO) algorithm. The results from a practical 122-bus, 24 kV/400 V distribution system with different scenarios of uncontrollable and smart charging show that the sequential optimization embedded with deterministic decision can help improve customer voltage profile, keep feeder and transformer loading within acceptable operating limits and offer significant cost savings from energy loss. As far as a large number of low-voltage networks, and the associated large sum of cost savings are concerned, the proposed planning framework is practical to be applied and expected to be served as a new guideline for future implementation in Thailand.

Forecasting e-scooter substitution of direct and access trips by mode and distance

An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New York City. A log–log regression model is estimated for e-scooter trips based on user age, population, land area, and the number of scooters. The model predicts 75 K daily e-scooter trips in Manhattan for a deployment of 2000 scooters, which translates to 77 million USD in annual revenue. We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternative modes of transportation that they would likely substitute based on statistical similarity. The model parameters reveal a relationship with direct trips of bike, walk, carpool, automobile and taxi as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters could replace 32% of carpool; 13% of bike; and 7.2% of taxi trips. The distance structure of revenue from access/egress trips is found to differ from that of other substituted trips.

A Novel Design of Photovoltaic-Based Charging Station for Battery Vehicles with Dynamic Demand: A Case of Short Runs

In this research, a novel design and operation of solar-based charging system for battery vehicle for a 50 km run is proposed. The proposal is aimed at replacing 110 existing diesel vehicles with 39 electric buses. Several operation scenarios for the charging stations are proposed and analyzed. Scenarios include two different battery charging methodologies and one hybrid option between electric buses and diesel vehicles. An energy model of the adapted electric buses is developed first. After that, load demand and needs including number of daily trips, number of passengers per hour, and hourly energy consumption are determined based on the developed model and gathered information. Results show that a 5.7 MWp photovoltaic system is required to power this transportation line with a loss of load probability of 5% and a trip cost per passenger of 2.05 USD. The simple payback period of the system is found to be 10 years, which is 40% of the system’s lifetime. The amount of CO2 mitigated by the proposed system is estimated as 1,629,387 (kg/year). The social impact of the proposed project is found acceptable; whereas, most of the current employees will keep their jobs with higher salaries by about 145% and less working hours by 50%. Moreover, it is expected that the proposed project will significantly increase the reliability, convenience, and sustainability of the transportation process.

Towards a novel set of GPS-derived metrics to identify the differences between mobility patterns of cognitively intact older adults and older adults with dementia

<h3>Introduction</h3> Maintaining an active lifestyle and participating in social activities are key components of healthy ageing. These components rely on the individuals' ability to remain mobile out of home. The global positioning system (GPS) is increasingly used to assess outdoor mobility of older adults. However, there is a gap in establishing a framework that recognizes the differences between mobility patterns of cognitively intact older adults and older adults with dementia (OAwD). We propose a set of GPS-derived metrics to identify these differences, which can be used to evaluate changes in mobility patterns of OAwD over time. <h3>Methods</h3> We analyzed the mobility profiles of 15 older adults from the greater Toronto area using GPS technology. Participants were aged between 65 and 90 years (M=74.9, SD=7.05) and were either cognitively healthy (n = 8) or diagnosed with dementia (n = 7). First, we created GPS-based features representing different dimensions of outdoor mobility. These features include (1) the typical distance covered by individuals, (2) randomness of the travel patterns, (3) the spatial variation of the GPS locations, (4) the number of distinct stops visited per day, (5) the number of daily trips away from home, (6) the score of three levels of outdoor life-space, and (7) the maximum distance travelled from home. Then, we used Recursive Feature Elimination (RFE) with Random Forest (RF) to select the most important mobility features with respect to cognitive status. <h3>Results</h3> A total of 55,580 GPS points were collected by the 15 unique participants over a period of 4 to 8 weeks. The RFE method indicated that features (1), (2), (3), and (5) had the strongest association with cognitive status. Our results suggested that OAwD displayed more predictability (i.e. smaller randomness) in travel patterns compared to the controls (OAwD: M=4.82, SD=0.57 vs. CTL:M=5.62, SD=0.71). Furthermore, the OAwD made fewer daily out-of-home trips compared to the controls (OAwD: M=1.06, SD=0.79 vs. CTL: M=1.65, SD=0.45), and covered smaller distances compared to the controls (OAwD: M=12.27, SD=11.73 km vs. CTL: M=15.78, SD=24.31 km). <h3>Conclusions</h3> The proposed set of GPS-based features identified the differences between mobility patterns of cognitively intact older adults and OAwD. <h3>Funding</h3> Canadian Consortium on Neurodegeneration in Aging

COVID-19 lockdown measures and travel behavior: The case of Thessaloniki, Greece

In this paper, we investigate the travel behavior changes in Thessaloniki, Greece aiming to understand them and explore the factors that affect them under the COVID-19 mobility restriction measures. Socioeconomic and mobility data from two questionnaire surveys, one year before and during the COVID-19 lockdown of April 2020 (with 1462 and 196 responses respectively), were compared by utilizing a wide variety of inductive statistical tests. Ordinary Least-Squares regression models and Cox proportional hazards duration models were employed to explore any concurrent socioeconomic effect on travel behavior patterns. Results showed that the number of daily trips per person was on average decreased by 50% during the lockdown. This decrease was much greater for the non-commuting trips. Trips on foot were increased, private car was mainly used for commuting and public transport modal shares were heavily reduced. Trip durations were generally increased, as travelling was considered a recreational activity per se. The starting times of the first trips of the day were more evenly distributed throughout the day and many travelers only started their first trips late in the afternoon. Older travelers generally maintained their mobility behavior patterns despite their higher vulnerability to COVID-19 disease. Lower-income travelers were likely to make more daily trips. Male travelers tended to make higher-duration trips compared to their female counterparts. Since pandemics may become recurring events in the future, our findings provide for a better understanding of their influence on mobility and support the design of customized policies to fulfill sustainable mobility objectives during lockdown circumstances.

Multiple performances and paradoxical effects of China's vehicle emission reduction policy.

As the living standards of urban residents in China continue to improve, the number of motor vehicle trips is increasing, thus aggravating air pollution. Such pollution causes great harm to human health and the global environment. Using a system dynamics approach, this study analyzed the effect of implementation mode on China's air pollution charging fee (APCF) policy and identified potentially negative medium- and long-term effects. The results indicated that the APCF policy has a dual effect under the single-charge mode (i.e., fees are charged on a daily basis). On the one hand, it has multiple effects of reducing emissions, relieving traffic congestion, and improving the happiness index. On the other hand, the higher the charge, the stronger the trip demand (possibly due to the sunk-cost fallacy and loss-aversion effect), which encourages motorists to weaken the cost of losses (i.e., from air pollution fees) by increasing the number of trips per day to seek short-term psychological balance, regardless of the extra costs and the amount of pollution generated. It was also found that APCF implementation mode significantly affected passenger car trips but not truck trips (perhaps because truck trips are mainly based on the demand of supply, and the daily number of trips is relatively stable). Overall, as APCF increases, it can have some paradoxical long-term effects on emissions, congestion, the happiness index, and road bearing capacity. This study's findings can help the Chinese government improve and optimize its long-term air pollution control strategies.

Impact of environment on bicycle travel demand—Assessment using bikeshare system data

The paper aims to quantify the daily bicycle traffic volume based on the data from bicycle automatic counters and bikeshare system (BSS) trip data taking into account the impact of weather conditions. Changes in bicycle traffic were assessed based on coefficient of variation of bicycle volume and number of BSS trips. The research was conducted in the city of Cracow (Poland) for 2 years. The volume at 13 locations with bicycle automatic counters and a daily number of trips on public bicycles were avaialble. Data on daily weather conditions were collected from a weather station located in the city.The monthly coefficients of variation of the number of BSS trips and cyclists’ volume in analyzed locations are not statistically significantly different. It means data from BSS, easily to obtain, can be used to assess variation in cyclists volume. Mean daily air temperature, daily rainfall, public and school holidays had a statistically significant impact on bicycle volume. Models have shown that BSS trip data can be successfully used to evaluate the impact of weather conditions on bicycle volume. Additionally, based on the developed models, reliable coefficient of variations for Annual Average Daily Bicycle Traffic of BSS users and all cyclists were indicated.

Towards a novel set of GPS‐derived metrics to identify the differences between mobility patterns of cognitively intact older adults and older adults with dementia

AbstractBackgroundMaintaining an active lifestyle and participating in social activities are key components of healthy ageing. These components rely on the individuals’ ability to remain mobile out of home. The global positioning system (GPS) is increasingly used to assess outdoor mobility of older adults. However, there is a gap in establishing a framework that recognizes the differences between mobility patterns of cognitively intact older adults and older adults with dementia (OAwD). We propose a set of GPS‐derived metrics to identify these differences, which can be used to evaluate changes in mobility patterns of OAwD over time.MethodWe analyzed the mobility profiles of 15 older adults from the greater Toronto area using GPS technology. Participants were aged between 65 and 90 years (M=74.9, SD=7.05) and were either cognitively healthy (n = 8) or diagnosed with dementia (n = 7). First, we created GPS‐based features representing different dimensions of outdoor mobility. These features include (1) the typical distance covered by individuals, (2) randomness of the travel patterns, (3) the spatial variation of the GPS locations, (4) the number of distinct stops visited per day, (5) the number of daily trips away from home, (6) the score of three levels of outdoor life‐space, and (7) the maximum distance travelled from home. Then, we used Recursive Feature Elimination (RFE) with Random Forest (RF) to select the most important mobility features with respect to cognitive status.ResultA total of 55,580 GPS points were collected by the 15 unique participants over a period of 4 to 8 weeks. The RFE method indicated that features (1), (2), (3), and (5) had the strongest association with cognitive status. Our results suggested that OAwD displayed more predictability (i.e. smaller randomness) in travel patterns compared to the controls (OAwD: M=4.82, SD=0.57 vs. CTL: M=5.62, SD=0.71). Furthermore, the OAwD made fewer daily out‐of‐home trips compared to the controls (OAwD: M=1.06, SD=0.79 vs. CTL: M=1.65, SD=0.45), and covered smaller distances compared to the controls (OAwD: M=12.27, SD=11.73 km vs. CTL: M=15.78, SD=24.31 km).ConclusionThe proposed set of GPS‐based features identified the differences between mobility patterns of cognitively intact older adults and OAwD.

Travel diaries, GPS loggers and Smartphone applications in mapping the daily mobility patterns of students in an urban environment

Abstract Mapping the daily spatial mobility of university students in an urban environment is the focus of this paper. It uses the city of České Budějovice in the Czech Republic as a case study, employing three different research tools – travel diaries, GPS loggers and Smartphone applications. We focus our attention on the analysis of spatial patterns of mobility using basic mobility indicators (distance, number of daily trips, time spent mobility), travel behaviours (use of transport modes) and the detection of time-space bundles (spaces of concentration of particular time-space trajectories) within the city. We identified four main time-space bundles. Then we compare the three main research methods according to their tracking accuracy and informative value. The Smartphone applications (using the A-GPS technology) provided the best results for the spatial mobility of respondents, although the travel diaries method is still unique due to the extent of some socio-demographic and transport characteristics.

Identifikasi Perilaku Perjalanan Melalui Metode Critical Path Method (CPM)

Mobilization in urban areas has increased every year along with the population growth and urban's development. Travel behaviors of the population in urban areas can be classified based on their travel characteristics. The identification of communities' travel behavior of Yogyakarta is based on the origin - destination and length of trip based on age characteristics. It will effects the travel patterns in the urban environment through spatial mapping. The method used in this research is critical path method. Critical Path Method (CPM) is used to determine the optimum path taken by the community based on its duration, which in this study CPM is used to determine the average travel of the community based on age groups when viewed from distance, length of trip and the number of daily trips. Based on the analysis, it shows that the average age of 15-22 years and 31-37 years have the highest travel time, which is about 9 hours and the average age of 23-30 years traveled the longest by 12.5 km. The average mobilization of travel behaviors in KPY is to the urbancenter of Yogyakarta. The KPY urban form does not have a compact form.

Impact of ride-hailing apps on traditional LAMAT services in Asian developing cities: The Phnom Penh Case

Abstract This study examines the impact of ride-hailing apps (RHAs) on traditional services of LAMAT drivers, who have not adopted RHAs, and investigates whether such impact would encourage the drivers’ intention towards adoption of RHAs. We analyzed the survey data collected from 177 drivers of traditional auto-rickshaws in Phnom Penh, December 11–14, 2018, as a case study. Comparison results showed that, after the advent of RHAs, the drivers experienced substantial reduction in number of daily trips by 47.7%, number of daily passengers by 47.6%, and monthly revenue by 43.2% (p

The influence of campus characteristics, temporal factors, and weather events on campuses-related daily bike-share trips

Recently, there has been an increase in bike-sharing programs on university campuses. However, there is scarce literature on the campus characteristics that are most favorable for these programs. This study evaluates the impacts of campus characteristics, temporal factors, and weather events on daily bike-sharing trips within U.S. university campuses. The study used data collected between 2014 and 2018 from 177 stations located within 25 campuses. With an average of 38 trips per day, a high variability in the number of daily trips was observed, with a maximum of 708 and a minimum of zero. Both traditional Negative Binomial (NB) and Mixed Effect Negative Binomial (MENB) models were applied, and their performances were compared based on a set of criteria. The MENB performed better than NB. The MENB model results revealed that among campus related factors, number of faculty/staffs, number of full-time students, private universities, campus size, and Latino and Asian student dominance are positively associated with an increase in trips per day, whereas number of part-time students, distance from campus to central business district, and students' ages are negatively associated. The temporal factors depicted that weekends, public holidays, time between semesters, and summer semester were associated with decreases in number of daily trips, while years in operation and fall semesters were positively associated with daily ridership. Severe weather events resulted in less bike-sharing, and average daily temperature was positively associated with number of trips. Campus bike-sharing planners can utilize these findings for estimating daily bike-sharing ridership on campuses.

Homelessness vis-à-vis Transportation-Induced Social Exclusion: An Econometric Investigation of Travel Behavior of Homeless Individuals in Toronto, Canada

The intersection of homelessness and transportation is an overlooked component of transport-related exclusion research, yet mobility is recognized as playing a significant role in anyone’s well-being. The lack of research in this field is concurrent with the lack of understanding of homeless individuals’ mobility needs and travel behavior. As a result, there is little that can guide the policy-making process for the travel needs of homeless individuals. In 2015, as a response to this research gap, an interview was designed and then conducted with 159 homeless individuals across eight shelters and drop-in agencies in the city of Toronto, Canada. The interview included stated adaptation experiments to reveal how transportation affected or hindered homeless individuals’ activity participation processes. Decision choice models were estimated with econometric modeling methods, which indicated the influence of variables such as age, income, and duration of homelessness on the decision to reject or accept employment opportunities. The results also showed that a person’s accustomed mode choice and daily number of trips had an effect on the decision to travel for social activity purposes.

Synergistic neighborhood relationships with travel behavior: An analysis of travel in 30,000 US neighborhoods

A now substantial body of literature finds that land use and urban form have a statistically significant, albeit relatively modest, effect on travel behavior. Some scholars have suggested that various built-environment characteristics influence travel more in concert than when considered in isolation. Yet few previous studies have combined built-environment measures to create holistic descriptions of the overall character of neighborhoods, and fewer still have related these neighborhoods to residents’ travel decisions. To address this gap in the literature, we develop a typology of seven distinct neighborhood types by applying factor analysis and then cluster analysis to a set of 20 variables describing built-environment characteristics for most census tracts in the United States. We then include these neighborhood types in a set of multivariate regression models to estimate the effect of neighborhood type on the travel behavior of neighborhood residents, controlling for an array of personal and household characteristics. We find relatively little variation in the number of daily trips among neighborhood types, but there is substantial neighborhood variation in both person miles of travel and mode choice. Travel by residents of one particular neighborhood type is notably distinguished from all others by a very low number of miles traveled, little solo driving, and high transit use. However, this neighborhood type is found almost exclusively in just a few very large metropolitan areas, and its replicability is uncertain.

Modeling significant factors affecting commuters’ perspectives and propensity to use the new proposed metro service in Doha

The Qatari government introduced a major public transport project titled the Doha Metro system to address the fast growing transportation demands in Qatar’s urban areas and to be ready for the Qatar 2022 FIFA World Cup. To benefit from this new metro system in reducing traffic congestion problems in Doha, it must be attractive with a reasonable level of service to attract large numbers of car users to switch to the new metro. This goal can be achieved by a better understanding of the user’s needs and expectations in Qatar. This paper aims to identify and quantify the significant factors affecting commuters’ perspectives, preferences and tendencies to use this new metro network for their daily trips in the future. The data used for the analysis was obtained from a self-reported questionnaire survey carried out among a sample of commuters living in Doha. Different data mining techniques were employed including conditional distributions and two-way analysis. In addition, logistic regression and structural equation modeling approaches were developed. The results revealed that the location of metro stations, the metro station’s features, the metro’s features, gender, the number of daily trips, the purpose of trips, and the average duration of trips in Doha were the significant factors that affected commuters’ willingness and tendency to use the new metro system. The results of this study provide authorities and decision makers in Doha with valuable insights that should be taken into consideration prior to implementing the new metro service to ensure its success.

Sustainable City-Plan Based on Planning Algorithm, Planners’ Heuristics and Transportation Aspects

A multi-agent model is applied to develop and asses the impact of urban forms in terms of sustainability measures. The aim of this model is to understand and draw conclusions concerning the planner's options to arrive at a plan, which reflects a sustainable built environment in terms of transportation, distribution of facilities, and minimization of pollution emission from vehicles. The relationships between transportation, land-use, and the environment are key factors for developing a sustainable built environment. The fundamental question is: What is the optimal city-form where equilibrium is reached? Of course, no single answer can be given due to the many aspects which come into play, such as city size, distance to other cities or to service centres. However for each city size and population preferences, a specific city form should lead to a sustainable environment. In this study, we suggest that the multi-agent model can be a useful support tool for planners to assess sustainability issues in their plan development. We examine city plans that differ in their main road-network. Two city forms/structures are considered, and these are the “compact city” and the “multi-nuclear city”. The land-use distributions that are obtained reflect these road/city-form alternatives, combined with input data about people preferences. This leads to the possibility to simulate distribution of facilities that takes into account predicted activity-travel patterns of individuals, using an activity-based model of transport demand, estimated on the basis of a comprehensive survey. The analysis of travel in the city, in turn, leads to the evaluation of parameters such as total travel distance per day, number of daily trips as well as accessibility characteristics of the planned city. This enables an analysis of pollution emission from vehicles and comfort of accessibility. Planner heuristics is incorporated in between calculation stages so that the procedure as a whole would lead to a realistic and desirable result. The presented model which is a combination of a theoretical simulation and realistic planner input can be used in urban planning actual practice. Relevant planning examples where this procedure can be used are new master plans for actual cities, in which major population increase is expected, or in a new development (neighborhood, city etc).