Central Business District Area Research Articles

Application of Connected and Automated Vehicles in a Large-Scale Network by Considering Vehicle-to-Vehicle and Vehicle-to-Infrastructure Technology

Application of connected and automated vehicles (CAVs) is expected to have a significant impact on traffic safety and mobility. Although several studies evaluated the effectiveness of CAVs in a small roadway segment, there is a lack of studies analyzing the impact of CAVs in a large-scale network by considering both freeways and arterials. Therefore, the objective of this study is to analyze the effectiveness of CAVs at the network level by utilizing both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. Also, the study proposed a new signal control algorithm through V2I technology to elevate the performance of CAVs at intersections. A car-following model named cooperative adaptive cruise control was utilized to approximate the driving behavior of CAVs in the Aimsun Next microsimulation environment. For the testbed, the research team selected Orlando central business district area in Florida, U.S. To this end, the impacts of CAVs were evaluated based on traffic efficiency (e.g., travel time rate [TTR], speed, and average approach delay, etc.) and safety surrogates (e.g., standard deviation of speed, real-time crash-risk models for freeways and arterials, time exposed time-to-collision). The results showed that the application of CAVs reduced TTR significantly compared with the base condition even with the low market penetration level. Also, the proposed signal control algorithm reduced the approach delay for 94% of the total intersections present in the network. Moreover, safety evaluation results showed a significant improvement of traffic safety in the freeways and arterials under CAV conditions with different market penetration rates.

City cents: Tracking the spatial imprint of urban public expenditures

Where is public money for place-based improvement spent? A common narrative is that public expenditure prioritizes business and development interests at the expense of disadvantaged areas that have the greatest need. This paper is a quantified look at this question—an analysis of spending patterns at the neighborhood level. The spatial analysis of public expenditure at the neighborhood level is surprisingly rare, in part because the data is not conducive to locational assessment at a detailed level. We posit three explanations for the pattern of expenditure: efficiency, pro-development, or equity/need. We find that place-based public expenditure in Chicago is unrelated to efficiency or equity/need, and mostly follows a pattern of spatial selectivity in which business and economic development are prioritized, primarily in and around the central business district and higher income residential areas. Our study augments earlier findings in that funding is considered in a spatially explicit manner at a highly disaggregated level of analysis.

Comparing associations among sound sources, human behaviors, and soundscapes on central business and commercial streets in Seoul, Korea

This study focuses on how urban functions influence the soundscape in streets by exploring associations among human behaviors, soundscape, acoustic indicators. On-site soundscape assessments were conducted in eight urban streets located in Seoul employing a soundwalk method. The eight streets were classified into two place functions: central business district (CBD) and commercial areas. Through the soundwalks, acoustic recordings and perceptual responses to soundscapes in the streets were collected. As a human behavioral indicator, crowd density was calculated in the streets. The results showed that place function plays a critical role in soundscape evaluation in urban streets. Pleasantness decreased with increasing Eventfulness on streets in CBD. In contrast, Pleasantness had a positive correlation with Eventfulness on commercial streets. This difference in the correlation could be attributed to the difference in the compositions of sound sources between central business and commercial areas. Traffic noise was the most dominant sound source in CBD, which increased Eventfulness and decreased Pleasantness of the soundscape in the streets. Meanwhile, sounds from human activities contributed to an increase in both Pleasantness and Eventfulness in commercial streets. Predictive models of soundscape reflecting the contexts of CBD and commercial streets were developed using acoustic parameters and crowd density. In the context of CBD, acoustic parameters describing spectro-temporal characteristics of traffic noise were important indicators, whereas crowd density was found to be a critical indicator to predict soundscape in the commercial streets. These findings could help in better understanding the relationships between the soundscape and place functions in urban streets.

Developing Parking Accumulation Levels to Formulate Land-Use Based On-Street Parking Policies in a Indian city - A case study

Demand for on-street parking is function of land use and therefore, it becomes imperative to investigate the effect of land-use type on on-street parking characteristics for developing land-use based on-street parking policies. To comprehend the aim, two diverse representative land-use types namely commercial and hospital were selected in the central business district (CBD) area of Rajkot city, India. Microscopic investigation was carried out by collecting demand data at every 10min monitoring interval during peak parking hours using license plate method. Investigation revealed that, land-use type significantly affected on-street parking characteristics and therefore, it becomes necessary to develop land-use based on-street parking policies. Parking index was identified as potential measure to formulate land-use based on-street parking policies, which was outlined into different levels representing parking accumulation levels (PAL). In context to the developed accumulation levels based on land-use type, the study proposes to formulate on-street parking policies based on turnover, if parking index delineates in PAL A and PAL B respectively. However, if parking index encompasses other than PAL A and PAL B, then only accumulation of parking demand should be considered for formulation of paid parking policies. The developed PAL provides a common platform for viewing on-street parking as a land-use based phenomenon.

Analisis Pemanfaatan Jalur Pedestrian pada Kawasan CBD Kota Makassar

Many phenomena arise in the city, which boils down to the problem of pedestrian pathways that are ineffective and neglected to use them. The first: Knowing the factors that cause pedestrian paths do not use well, Secondly: Find the formulation of the model of application of environmental based walkability in accordance with the rules per city design. The location of the research is Jalan Sultan Hasanuddin to Jalan Pattimura and Jalan Slamet Riyadi which are integrated with the CBD (Central Business District) area with a research method combining qualitative and quantitative benefits. The research variables are physical comfort, spatial-visual comfort and non-physical comfort. The analysis was conducted with descriptive, discussion analysis and simple regression based on the significance value of the trial (α) = 0.05. The results showed that physical comfort variable (X₁) simultaneously had a strong relationship to comfort level (Y), whereas spatial-visual comfort (X₂), and non-physical comfort (X₃) had a weak relationship to comfort level. One of the factors that influence the comfort of the pedestrian path in the area of Sultan Hasanuddin Street - Pattimura Street - Slamet Riyadi Street is the aspect of physical comfort. For the formulation of a Walkability-based environmental concept, in general, namely The provision of pedestrian paths must connect, The need for coordination with related parties regarding the provision of pedestrian paths, Coordination of each party regarding the provision of supporting facilities.

등급별 오피스 빌딩의 순 실효 임대료에 영향을 미치는 주변 지구 및 대상 물건 요인 분석 : 서울시 도심 오피스를 대상으로

이 논문은 주저자의 석사학위 논문을 기반으로 수정·보완하여 작성하였으며, 2016년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF-2016R1A2B4010260).

Internet of Things data analytics for parking availability prediction and guidance

AbstractCutting‐edge sensors and devices are increasingly deployed within urban areas to make‐up the fabric of transmission control protocol/internet protocol connectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud‐based services. Connected vehicles and road‐infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic‐congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov‐chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search process is triggered to identify the expected arrival‐time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart‐pole data streams reporting congestion rates across parking area junctions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state‐transition matrix, showing the transformation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy‐expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capabilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban‐mobility simulation packages, the traffic‐congestion‐aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.

Assessment of the Integrated Personal Exposure to Particulate Emissions in Urban Micro-environments: A Pilot Study

City dwellers’ personal exposure to PM is influenced by numerous daily activities in multiple indoor and outdoor micro-environments (MEs). This study assessed the integrated personal exposure to PM across urban MEs together with the recording individual time-activity patterns. We evaluated simultaneously the exposure to PM2.5, black carbon (BC), and ultrafine particles (UFPs) in the Central Business District (CBD) area of Singapore. In addition, we quantified the lung-deposited surface area (LDSA) concentration, which is an indicator of the potential health impacts of UFPs. The field study was conducted over a 7-km walking route to identify air pollution hotspots. Subsequently, the personal exposure to PM2.5, BC, and UFPs was measured at five selected hotspots for 1 hour each and across indoor and outdoor MEs during diverse daily human activities for 24 hours. The PM concentrations were found to vary considerably in both space and time in the CBD area. During the 1-hour personal exposure measurement, extremely high concentrations of PM2.5 (215 ± 129.5 µg m–3 and 36.4 ± 12.5 µg m–3) and BC (20.9 ± 10.4 µg m–3 and 18.1 ± 12.0 µg m–3) were observed at a temple and a bus stop, and elevated UFP number concentrations (320.8 ± 131.1 × 103 # cm–3) and high LDSA concentrations (564.6 ± 276.5 µg2 cm–3) were measured at a food court. The estimated potential health risk suggests that the continued inhalation of large amounts of PM2.5 emitted from combustion sources is likely to lead to adverse long-term health effects among the exposed individuals. Overall, we provide insight into an individual’s total exposure to PM based on time-activity patterns. The results of this work form a scientific basis for developing air pollution control measures to mitigate personal exposure to PM on a city scale.

Traffic Impact Assessment for Sustainable Development in Urban Areas

Since long, Traffic Impact assessment (TIA) has been recognized as an integral and mandatory part of Environmental Impact assessment in developed countries. Accordingly, it is mandated that any proposed development expected to generate 100 or more additional / new trips during peak hours should undergo a TIA However, in developing countries like India, TIA has gained importance only during the last decade spearheaded by the need to develop sustainable solutions to the congestion problem. In this paper we discuss the findings of two case studies one being an official complex coming up close in the Central Business District (CBD) area of New Delhi, India and the other being the assessment of the impact due to proposed extension of an Information Technology (IT) Park located at the Special Economic Zone at a distance of 4 Kms from the corporation limits along the eastern periphery of city of Madurai, Tamilnadu, India. The case studies highlight the importance of TIA and bring out the need to make TIA an integral and mandatory requirement in the case of new as well as capacity augmentation.

Development of Bicycle Safety Index Models for Safety of Bicycle Flow at 3-Legged Junctions on Urban Roads under Mixed Traffic Conditions

In Urban areas of developing countries like India, there have been a rapid increase in the bicycle volumes and traffic-bicycle conflicts during the last few decades. To enhance bicycle safety under mixed traffic conditions, there is a need to improve the bicycle facilities on the urban roads at un-signalized 3-legged junctions. The present study aims to develop bicycle safety index models using variables like bicycle volumes, bicycle speed (km/min), bicycle markings and bicycle safety ratings. The data was collected at locations of different 3-legged junctions in the central business district area of Srinagar City in India. At these selected locations, bicycle volume count and bicycle average speeds (km/hr) were observed during peak hours. Bicycle flows were categorized based on different age groups, gender, bicycle speed and direction. The bicycle safety index models were calibrated and validated using the collected data. The model results confirm estimation of correct bicycle safety levels at un-signalized 3-legged junctions. The study will also be helpful to improve the existing bicycle facilities, bicycle flows (bikes/hour) and also, to provide bicycle safety measures at un-signalized 3-legged junctions on urban roads in mixed traffic environment.

Analysis of One-Way System Implementation with Contra-Flow Bus Lane in Supporting Sustainable Transportation Program

Traffic development in the central business district area along the Brigjend Slamet Riyadi road, Surakarta City, Indonesia, is relatively dense and not in accordance with the development and changes in the environmental situation. This phenomenon creates congestion on several roads and intersections. Transport planning related to traffic management and engineering, and infrastructure needs to be carried out to solve urban transportation problems. Based on the Surakarta City Government’s vision related to sustainable transportation, the one-way system with contra-flow bus lane will be implemented along the Brigjend Slamet Riyadi road. With the existence of this system, it is hoped that public transport Batik Solo Trans as one solution of urban transportation problem can be more integrated, fast, safe, comfortable, reliable and efficient. The purpose of this research is to determine the impact of the implementation of one-way system with contra-flow bus lane to the traffic performance in the study area. The research methodology involves modelling and analysing the traffic performance, parking and potential public transport demand in the study area. The existence of one-way system with contra-flow bus lane has the potential to increase the use of public transportation and improve the performance of public transportation. In general, traffic performance decreases, therefore it is necessary to perform transportation demand management such as management parking.

Analysis of the acceptance of park-and-ride by users: A cumulative logistic regression approach

Park-and-ride (P&R) schemes are an important way of increasing the public transport mode share, which relieves the negative impact caused by excessive automobile usage. Several existing studies have been conducted in the past to explore the factors that can influence the acceptance of P&R by travelers. However, quantitative analyses of the pertinent factors and rates of traveler choice are quite rare. In this paper, the data collected from a survey in Melbourne, Australia, is used to analyze the acceptance of P&R by travelers going to the central business district (CBD). In particular, we explore the influence that specific factors have on the choice of travel by those who are currently using P&R. The results indicate that the parking fee in the CBD area, travel time on public transport, and P&R transfer time affect traveler use of P&R. A quantitative assessment of the impact of these three factors is conducted by using a cumulative logistic regression model. Results reveal that the P&R transfer time has the highest sensitivity while public transport travel time has the least. To maximize the use of P&R facilities and public transport, insights into setting parking fees and designing P&R stations are presented.

Data monitoring for the assessment of on-street parking demand in CBD areas of developing countries

On-street parking is considered as one of the essential and critical components of the urban transportation system. Despite urban sprawl, central business district (CBD) areas continue to attract significant number of trips, especially for shopping, trade and commerce purposes. Most of the cities in developing countries lack the mechanisms to monitor on-street parking demand. Further, the absence of unambiguous on-street parking policy has led to the phenomenon of largely disorganized on-street parking, especially in these cities. Therefore, to formulate parking management measures, it becomes imperative to assess existing on-street parking demand in a rational manner. To comprehend the objective, on-street parking survey using the license plate method was carried out for three days covering a normal working day, weekend and an event day at data monitoring interval of 10 min during peak parking hours for two diverse land-use types namely commercial and hospital in four metropolitan cities of Gujarat, India. Investigations revealed that the data monitoring interval significantly affected recorded parking demand. Recorded parking demand was observed to be insignificant after 30 min and 50 min data monitoring interval for commercial and hospital land-use respectively. The behaviour of parkers was probed by extracting percentage unique of parked vehicles (PUPV). PUPV revealed uniform parking behaviour in different cities, with progressively increasing trend as the data monitoring interval increased. Turnover (E2W/hr/bay) was evaluated for different data monitoring intervals and revealed a decaying trend with an increase in data monitoring interval. The consistency of PUPV (in form of the coefficient of variation (CV)) was observed at 30 min and 50 min data monitoring interval for commercial and hospital land-use respectively. Therefore, to capture all possible variation in on-street parking demand, the study proposes to perform on-street parking inventory and on-street parking evaluation at 30 min and 50 min data monitoring interval for commercial and hospital land-use respectively. The study provides a fundamental insight into the assessment of on-street parking demand using different data monitoring intervals and can accordingly facilitate planners and policymakers to formulate on-street parking demand management measures in developing countries.

Geospatial Analysis of Building Structures in Megacity Dhaka: the Use of Spatial Statistics for Promoting Data-driven Decision-making

Information on spatial building structures is limited, but it can support efficient planning and management in the context of fast-growing big cities in many developing countries. In this paper, we present a spatial analysis approach that includes an estimate of building intensity in the megacity of Dhaka and a spatial analysis using spatial statistics. The entire city was divided into regular grids and the building intensity (both horizontal and vertical) was extracted using vector type building information; the spatial statistics were calculated on the basis of Moran’s I and Gini indices. The variability of the estimated spatial statistics is interpreted according to co-relationship or clustering patterns with the location of the central business district (CBD) area as well as the public bus transit infrastructure (routes and stops). The results show that the residential building structure intensity is prominent and the concentrations are distributed all over the city. The mixed-use type building structures show highest clustering, with fewer outliers in the old part of the city. The vertical-use intensities indicate extreme clustering within highly intensified building activity in the nearby CBD area. The higher presence of low-low clustering of horizontal intensity indicated low development at the suburban area. However, the strongly clustered grid cells within residential sector as well as horizontal development classes are less accessible by bus transit within a defined catchment area, whereas the service sector and vertical development type seem to be more accessible. This type of geographic approach, visualization, and statistical information can help in making data-driven planning decisions with the advantage of monitoring urban development; however, the modeling sensitivity and uncertainties in the building data set remain open for further investigation.

Understanding and Analysing the Urban Heat Island (UHI) Effect in Micro-Scale

The shortage of vegetation cover alongside urban structures and land hardscape in cities causes an artificial temperature increase in urban environments known as the urban heat island (UHI) effect. The artificial heat stress in cities has a particular threat for usability and health-safety of outdoor living in public space. Australia may face a likely 3.8°C increase in surface temperature by 2090. Such an increase in temperature will have a severe impact on regional and local climate systems, natural ecosystems, and human life in cities. This paper aims to determine the patterns of the UHI effect in micro-scale of Adelaide metropolitan area, South Australia. The urban near-surface temperature profile of Adelaide was measured along a linear east-west cross-section of the metropolitan area via mobile traverse method between 26 July 2013 and 15 August 2013. Results indicate that the while the maximum UHI effect occurs at midnight in the central business district (CBD) area in Adelaide, the afternoon urban warmth has more temperature variations (point-to-point variation), especially during the late afternoon when local air temperature is normally in its peak. Thus, critical measurement of heat-health consequences of the UHI effect need to be focused on the afternoon heat stress conditions in UHIs rather than the commonly known night time phenomenon. This mobile traverse urban heat study of Adelaide supports the hypothesis that the UHI effect varies in the built environment during daily cycles and within short distances. Classical UHI measurements are commonly performed during the night – when the urban-rural temperature differences are at their maximum. Thus, they fall short in addressing the issue of excess heat stress on human participants. However, having thermally comfortable urban microclimates is a fundamental characteristic of healthy and vibrant public spaces. Therefore, urban planning professionals and decision makers are required to consider diurnal heat stress alongside nocturnal urban heat islands in planning healthy cities. The results of this article show that the diurnal heat stress varies in the built environment during daily cycles and within short distances. This study confirms that the maximum urban heat stress occurs during late afternoon when both overall temperature and daily urban warmth are at their peak. Literature indicates that diurnal heat stress peaks in hard-landscapes urban settings while it may decrease in urban parklands and near water bodies. Therefore, urban greenery and surface water can assist achieving more liveable and healthy urban environments (generalisation requires further research). A better understanding of daily urban warmth variations in cities assists urban policy making and public life management in the context of climate change.

Study on the layout method of Urban underground parking system-a case of underground parking system in the Central business District in linping New City of Hangzhou

In the face of imbalance between supply and demand of parking facilities in urban central business district (CBD) areas and the increasingly prominent parking problems, it has become necessary to develop and construct underground parking systems so as to alleviate ground traffic pressure and reduce traffic congestion. Underground parking system planning includes the following main components: system level division, parking scale determination, entrance and exit settings, and internal flow organization of the system. By analyzing the current parking characteristics of the CBD in Linping New City of Hangzhou, and based on indicators such as base conditions of the underground parking system, the total number of parking berths and the number of parking units, a scoring standard is used to determine a grading method for the underground parking system. According to different levels of underground parking system, and based on the number of system parking units and the number of vehicles entering and leaving the system during peak hours, the principle of setting the number of entrances and exits of underground parking system is clearly defined. According to the layout pattern of underground parking system and the internal traffic flow analysis of the vehicles entering and leaving the system, the setting of underground vehicle lanes and underground parking units in the system is taken into account to determine the internal traffic organization of underground parking system. This research provides a reference for the planning and design of urban underground parking systems.

How accurate is the regional travel demand model in mimicking real-world travel times?

ABSTRACTThis paper provides insights pertaining to the validity of a regional travel demand model in mimicking real-world travel times. The estimated travel times from the regional travel demand model, for the base year 2015, for Mecklenburg County in the city of Charlotte, North Carolina (NC) were compared with travel time statistics from a private data source, for the same year. The results indicate that the estimated travel times from the regional travel demand model are typically lower than the 85th percentile travel times, irrespective of the link speed limit. The estimated travel times for the Central Business District (CBD) area type are moderately correlated with the travel time statistics from the private data source, irrespective of the time of the day. For all the other area types, stronger correlations were observed when the estimated travel times from the regional travel demand model are compared with 10th to 50th percentile travel times. The calculated Pearson correlation coefficients are low for morning and evening peak periods compared to mid-day and night-time period, indicating the inability of the regional travel demand model in mimicking congested traffic conditions accurately.

Evaluation of Structure and Accessibility of Public Space Network in Kano Central Area with the Bid to Promote Urban Cohesion and Sustainability

There have been major concerns in the planning of both public and private spaces in Kano central area. Consequently, the entire urban fabric have been restructured due to uncontrolled developmental growth, population density, high cost of land value, unavailability of business location and poor accessibility to road network. These situations have brought about a physical and social shift in the position of Kano Central Business District (CBD) area, of which not prevented will continue to cause major urban cohesion and sustainability issue. Thus, this paper took a preemptive step to evaluate the existing problems and identify the major sustainability issues and where urban cohesion failed. A qualitative method was employed that does not only consider the study of morphological factors of the public space network in Kano CBD, but also the social, environmental and economic dynamics they generate. Thus, the data were collected through fieldwork (favoring direct contact with the territory), and analyzed using four key dimensions namely ‘Form and legibility’, ‘Access and Connections’, ‘Uses and Activities’, then ‘Sociability and Identification’. The findings revealed that the neighborhoods that make up the CBD have lot of abandoned buildings, insufficient access routes, poor drainage system, high volume of traffic that cause lot of pollution and proliferation of solid waste that makes road network impermeable, and an aesthetic eyesore. In view of these, the study recommends a set of urban intervention strategy capable of guiding the planning and redesign of public spaces in Kano CBD in order to promote urban cohesion and a sustainable environment.

Influence of on-network, traffic, signal, demographic, and land use characteristics by area type on red light violation crashes

The focus of this research paper is on extraction of predictor variables pertaining to on-network, traffic, signal, demographic, and land use characteristics, by area type, and examining their influence on the number of red light violation crashes. Data for the city of Charlotte, North Carolina was extracted and used for analysis. Three different sets of signalized intersections were selected in the three different area types - Central Business District (CBD), urban, and suburban areas. Each set is comprised of sixty signalized intersections (total 180 signalized intersections). The number of red light violation crashes from January 2010 to December 2014, within the vicinity of each selected signalized intersection, was considered as the dependent variable to develop crash estimation models for each area type. The crash estimation models by area type were compared with the crash estimation model developed considering all the 180 signalized intersections together. Different predictor variables were found to be significant at a 95% confidence level in three different areas. Log-link model with Negative Binomial distribution was observed to best fit the data used in this research. Findings indicate that enforcement, either manually or using red light running cameras (RLCs), at signalized intersections with high traffic volume in the CBD area; at signalized intersections with high traffic volume, high all-red clearance time, near high density of horizontal mixed non-residential and open space/recreational type land uses in urban area; at signalized intersections with high traffic volume, speed limit on the major approach, the number of lanes on the minor approach, and all-red clearance time and areas surrounded with horizontal mixed non-residential and retail type land use in suburban areas, would lead to a reduction in the number of red light violation crashes.

Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested network

Deployment of a simulation-based dynamic traffic assignment (DTA) model requires calibration of a large number of demand and supply input parameters. In this paper, we present a data-intensive framework for deployment, calibration, and validation of a simulation-based DTA model of Melbourne, Australia as a large-scale congested network. The model consists of 55,719 links and 24,502 nodes and simulates almost 2.1 million commuters in a 4-h morning peak period. We propose a machine learning based technique to classify and calibrate the traffic flow fundamental diagrams against empirical data obtained from a large number of freeway loop detectors across the network. An optimization framework for estimating the time-dependent origin-destination (TDOD) demand is also presented. To enhance the quality of the TDOD demand estimation, we apply a departure time profiling technique to account for the spatial differences between OD pairs. The paper also demonstrates the impact of adaptive driving on the quality of the DTA calibration. A comparison between the simulated and observed link volumes over 1,250 locations across the entire network shows that the calibration procedure generates an approximately 30% improvement in the root mean square error (RMSE). The impact of pedestrians and cyclists on the vehicular traffic is also implicitly considered in the central business district (CBD) area to improve underestimation of the simulated travel times. Validation results suggest that the calibrated DTA model successfully replicates traffic patterns in the network and support the future applications of the model for various transportation operations and planning purposes.