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The coordination relationship between urban built environment and transport system is an indispensable field in the study of urban planning. Recent research efforts in built environment and transport system have focused on the effects of built environment on travel behaviors, such as car ownership, choice of travel mode, and travel frequency. These...
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Citations
... By dividing the city into built environments and road network spaces, existing studies found that the built environment has nonnegligible relationships with traffic congestion (Li et al. 2021). Data cross-correlation-based studies demonstrated a quantitative relationship between built environment factors and traffic congestion through the generalised regression model and deep learning (Qin et al. 2020;Nian et al. ...
... Due to the slow variation of the built environment in a short period, it mostly reflects the long-time spatial distribution of the traffic state rather than short-time variability. The dynamic population is widely distributed in the built environment space, and spatially distant travel destinations are reached through road networks (Li et al. 2021). The travel dynamics in the urban built environment, such as destination preferences and travel conditions, strongly influence the time-variability of the traffic state (Wu et al. 2019). ...
Urban road traffic congestion remains challenging due to global urbanisation and has caused travel delays, energy consumption, and detrimental emissions. Therefore, exploring the potential dominant factors associated with traffic congestion generation is necessary to mitigate traffic congestion. The built environment around congested areas is the core factor in the generation of traffic congestion, however, only a few considered the impact of human travel features on congested roads. We divided human travel factors into purpose- and movement-related factors and explored the nonlinear relationship between human travel factors and traffic congestion. The results from taxi travel in Wuhan show that travel purpose factors mostly impact traffic congestion on low-grade inner-city short roads, while movement factors mainly impact the periphery ring or high-grade long roads. Movement-dominant congestions are widespread but not severe. Severe traffic congestion occurs mainly due to purpose-dominant travel. For purpose-dominant congestions, all excessive POI visits may worsen traffic congestion, and higher POI mixing degree has a positive effect on reducing congestion. For movement-dominant congestions, the detour rate and congestion level show a positive dependence, and the whole travel distance and travel accomplished rate indicate a U-shaped nonlinear relationship with congestion. This study provides detailed partial dependence plots of how congestion varies with human travel factors, providing insights and locational indications for traffic participants and urban designers to reduce congestion and improve urban mobility.
... Future planning will solve the traffic congestion by reasonable planning of land use and controlling density in the urban city, hence the traffic congestion could be controlled and alleviated with urban planning and transportation system optimization [12] and [13]. Li et al. (2021) explored the relationship between the level of traffic and the developed environment, the obtained results showed the bus system and distance from the city center have a high impact on the coordination relationship [14]. The too-low or too-high polycentrism of land used induced excessive congestion and reduced the traffic operation of urban streets [15]. ...
... Future planning will solve the traffic congestion by reasonable planning of land use and controlling density in the urban city, hence the traffic congestion could be controlled and alleviated with urban planning and transportation system optimization [12] and [13]. Li et al. (2021) explored the relationship between the level of traffic and the developed environment, the obtained results showed the bus system and distance from the city center have a high impact on the coordination relationship [14]. The too-low or too-high polycentrism of land used induced excessive congestion and reduced the traffic operation of urban streets [15]. ...
This paper satisfies the requirements for reliable and inexpensive congestion detection in urban road networks. The objective of this research is to use fuzzy logic to detect the traffic conditions states based on sets of rules that compare the filed traffic states. An alternative approach that enables knowledge based on effective and efficient methods of detecting traffic congestion. The main strategies in this work include the detection of congestion based on index measures of traffic speed, index speed reduction, and speed ratio. Spatial analysis of traffic data utilizing ArcGIS application to produce digitized street maps of congestion assessments through GIS traffic data. Utilizing the Fuzzy Inference System (FIS) approach for adopted traffic parameters provides an analytical solution for ambiguous and uncertain problems. The categories of traffic input parameters distinguish states of congestion levels through the determination of values of congestion index levels. The analysis of the speed reduction index illustrated the hot spots within the study network which represent Bab Al-Moathum zone; Al-Mawal (Mustansiriyah University zone); Al-Kindi government hospital induced heavy congestion with an index range of (0.651-0.717). A precise threshold to describe the level of congestion using the Fuzzy Inference System to evaluate urban streets into different categories; Free-flow, Normal, Moderate, Heavy congested, and Blocked. A contribution of two input traffic parameters is considered to quantify congestion in one output that combines different congestion field measures. Over 15 links (Bab Al-Moathum zone, Palestine Street near Mustansiriyah University, Al-Kindi govern ate hospital with black color induced worse traffic conditions, resulting in a blocked effect. The rest of the segments within the case study range from heavily congested to normal. A more realistic and detailed view of traffic congestion for selected street networks is obtained based on a fuzzy inference system as to traditional methods that consider one parameter for traffic performance.
... Intelligent and quiet transportation planning reduces conflicts between pedestrians and vehicles on roads. The primary location of pedestrian life on the road should be determined with due respect to bicycles and cars to create "harmonious and common" road traffic (Li et al., 2021). By setting design criteria for density, clearance, coherence, and diversity of interfaces, the aim is to create a "public space" with an excellent spatial experience for urban roads. ...
INTRODUCTION: The function of many public street spaces in Chinese cities is declining, but urban street space is essential in cities. How to enhance the street's fireworks and reshape the street's rich living atmosphere is worthy of further research and discussion. OBJECTIVES: Based on the similarity algorithm urban street enhancement-related theories, paper summarizes the current problems of urban street space in China, researches the corresponding enhancement strategies according to the issues, and makes a strategic research and summary on the relationship between the interfaces of the scope of the visual field and the human behavior, as well as the relationship between the pedestrian and the vehicular traffic. METHODS: An in-depth study after defining the concept, summarizing the idea and extracting the urban street refinement design model using the similarity algorithm. RESULTS: The new urban street refinement design model can improve the psychological satisfaction of people walking in the application; the street space design model of the walking experience will also use the algorithm to simulate the joy; lastly, a recommended optimization technique is presented for the construction of a humanized street scale and other related factors. CONCLUSION: The study of urban street space is a refined design strategy for the improvement of the urban landscape; the growth of the happiness index of urban residents is of great significance and, at the same time, for the enhancement of China's modernization level, improve the human habitat environment are of great importance, and should pay attention to the urban street refinement design.
... Specifcally, the number of urban public facilities, commercial and residential facilities, and the density of the public transportation network all impact the trafc states in a region [15]. In addition, the distance from the city center impacts the coordination relationship between the built environment and trafc congestion [40]. Moreover, the urban built environment can impact urban residents' travel behavior [28] and the choice of residents' travel patterns may impact trafc congestion [41]. ...
In new-tier cities with rapid urbanization, the reorganization of urban spatial functions and the development of road networks have brought novel challenges to traffic congestion control. Urban land use patterns have a significant correlation with urban traffic congestion. However, whether and how land use patterns of cities close to the roads affect road congestion is less to be discussed. This article investigated the relationship between land use patterns close to the urban trunk road network and traffic congestion in new tier cities Xi’an, China. We adopted the DBSCAN algorithm to cluster POIs and use the mixed POI clusters to label the socio-economic functions of roads. We found the spatial heterogeneity of POIs on the trunk road network and identified the impacts of the scales and types of POI on road congestion based on the empirical analysis. Compared to the POIs as origin and destination of the trips, the POIs as stopover points of the trips cause significantly more road congestion. The POIs with bidirectional flows at entrances/exits are more likely to cause road congestion than the POIs with unidirectional flows. Moreover, the POIs with flexible traffic flows increase road congestion, while the POIs with predicted traffic flows have no statistically significant correlation with road congestion. The results help urban planners to plan the scale, type, and location of POIs close to roads and to optimize the socio-economic functions of roads and alleviate road congestion.
... Pattara-atikom et al. (2006) investigate a way to estimate degrees of road traffic congestion based on GPS measurements from main roads in urban areas of Bangkok, Thailand. Li et al. (2021) explored the relationship between the level of traffic and the developed environment, the obtained results showed the bus system and distance from the city center have a high impact on the coordination relationship. Zheng et al. (2016) studied the effect of the high occupancy rate of a large office building on road traffic and obtained good results, which explored how the built environment affects the state of traffic on adjacent roads. ...
... Pattara-atikom et al. (2006) investigate a way to estimate degrees of road traffic congestion based on GPS measurements from main roads in urban areas of Bangkok, Thailand. Li et al. (2021) explored the relationship between the level of traffic and the developed environment, the obtained results showed the bus system and distance from the city center have a high impact on the coordination relationship. Zheng et al. (2016) studied the effect of the high occupancy rate of a large office building on road traffic and obtained good results, which explored how the built environment affects the state of traffic on adjacent roads. ...
Road traffic congestion produces undesirable impacts on urban city
centers. Delays and air pollution are well-known negative examples of these
impacts and several policies have endeavored to reduce them. The congestion
indexes can assess the traffic congestion conditions of urban road networks,
more importantly, such an assessment study provides traffic control and
management agencies with an accurate and clear understanding of the operation
status of traffic networks. The objective of the present study is to analyze traffic
congestion of urban streets in Baghdad City 14-Ramadan Street is considered the most congested road due to mixed land uses. For this purpose, it is required to further develop the existing congestion indicators in order to make them suitable for a network evaluation. The speed performance index was adopted to evaluate the
existing road network conditions of congestion, and then road segment and network
congestion indexes were introduced to measure the congestion levels
of the urban road segment. Using a global positioning system, this study also carried out a traffic congestion analysis for 14 Ramadan Street, based on the speed performance data collected from February 2 and 11, 2021. Congestion of traffic
conditions is taking the major part during peak hours of the day from 6:00-8:00
p.m. on Tuesday 2 and Thursday 11 Feb. 20 which is selected to study the
variations of total travel speed for each link in the selected site of 14-Ramadan
Urban street. Global Positioning System (GPS) essential measurement equipped
with a cell phone is applied to record at peak period and a sample run of 20 is
provided for each link. The variation of travel traffic speed for the studied
corridor. And it can be seen that the variation of travel speed with the lowest
values for link 2. The proportion of speed performance for links 1 and 2 was more
than 50% with an index value of 70%, 77%, and below 50% with an index value
of 28 % for link 2. Evening peak hours with the lowest points at 6:30 and 7:30.
During the evening peak, the speed performance begins to decline quickly at 6:30
and then declines to the lowest point at 7:30, of about 22%, 20%, and 30% for
Links 1, 2, and 3 respectively, present very congested conditions. The speed
performance is better than the peak hours that appear around 6:00 p.m. This gives
a speed performance index of 65%, 55%, and 71% for links 1, 2, and 3
respectively which is greater than 50% that demonstrated relatively smooth traffic
flow conditions compared to peak periods. Geographic information systems
(GIS) are implemented and schematic maps are developed to display the average
travel speed; speed performance index; and congestion index of 14 Ramadan Street.
... Commonly used SUs in the study of BE and TSS interactions include the TAZ [19,20], buffer zone [21], and grid [22]. Considering the need for multiscale design of the SU scale and types of this study, we note that once the traditional TAZ division has been completed, it is difficult to change. ...
Spatially aggregated data are prone to the effects of the modifiable areal unit problem (MAUP), which applies to built environments and traffic data. Although various studies have been carried out to explore the impact of built environment factors on traffic systems, few have considered MAUPs, which may result in statistical inconsistency. The purpose of this study is to assess the effects of MAUPs on statistical variables and geographically weighted regression results when evaluating the influence of the built environment on the traffic system state. Fifty sets of spatial configurations were created using the different aggregation criteria. The variance inflation factor and spatial autocorrelation of the variables, as well as the R2 and root mean squared error of the GWR model, were used to assess the MAUP effect. The results show that the index variation is more dependent on the scale of the spatial unit than on zoning type. In the case study presented, based on the available dataset, the optimal spatial unit size for analyzing the influence of the built environment on Jinan’s traffic system was 900 m × 900 m.
... Later, study of the relationship among multi built environment elements, usually represented by seven D-variables (Ewing et al., 2010), travel behavior and traffic level. Li et al. (2021) applied Data Envelopment Analysis to analyze the coordination relationship between built environment and transportation system and concluded the greatest impact on the coordination relationship by using point of interests (POIs) and floating car data. Liu (2020) applied POIs data to enhance the spatial semantic information of traffic accidents, and explored the spatial semantic mode and causes of traffic accidents through visualization and interaction. ...
Establishing the relationship between built environment and traffic congestion states provides a feasible direction to explore factors affecting the spatial distribution of road traffic states, and supports to meet needs of traffic management and control under the information environment. The study analyzes the spatial pattern of traffic state, establishes two regression models to identify factors affecting local traffic congestion, and visualizes the impact degree of spatial heterogeneity. Firstly, the real data are collected and processed, and congested grids are selected as study range. Then, independent and dependent variables are obtained, presented in grids by ArcGis. Third, the ordinary least squares (OLS) and the geographically weighted regression (GWR) are applied to fitting. Results show the influence and the influential degree are different in spatial distribution. Analysis of the traffic state by GWR based on grids is feasible to present the traffic state.
... Metro station is the only operating node of the rail transit system connecting to other urban physical surroundings, so impacts of rail transit on urban industries and space firstly and most obviously appear in metro station areas [3], and industrial distribution in metro station areas is regarded as one of the most important research topics at a small scale in economic geography and urban planning. It is reported that an urban built environment around a traffic station always affects the staying time and experience of people who pass by it [4]. Accordingly, the better built environment a metro station area has, faster trips and more visits there will be both for businesses and personals [5] so that one kind of beneficial location condition is created for modern industries to survive and develop in this area. ...
... All the basic data about 3 space elements of 81 remaining metro station areas in Xi'an were obtained with the help of the above steps. e diversity index D in each area was then calculated by using equation (2), openness index K by (3), and street network density index P by (4). ...
Since impacts of rail transit on urban industries and space most obviously appear in metro station areas, correlations between the spatial distribution of producer services and urban built environment in metro station areas are studied in Xi’an, China. At first, the scope determining methods of a metro station area were separately proposed for both single-line station and multiline transfer station based on their construction and transportation modes. Then, when the producer services were divided into 6 categories of business, POI numbers of enterprises of each category in metro station areas were collected and weighted to calculate the distribution intensity for categories. Finally, on condition that the urban built environment of a metro station area included job and residence space, outdoor activity space, and municipal road space, correlation performances between the distribution intensity of categories and indexes representing 3 space elements of the built environment were calculated through second-order partial correlation analysis, while the corresponding mechanisms of correlations were explained too. The main conclusions of this paper indicate that there are significant positive correlations between the distribution intensity and the diversity index of job and residence space for most categories, whereas significant negative correlations between it and the openness index of outdoor activity space for most categories. Moreover, positive but weak correlations were appeared between the distribution intensity and the street network density index of municipal road space only for categories of intermediary consulting and leasing.
... Based on the close relationship between built environment and traffic congestion on adjacent roads, Qin et al. constructed a graph convolutional network model to predict road congestion using built environment indicators, and the model prediction results were consistent with the real road traffic status obtained from the GPS trajectories of taxis with about 85.5% [9]. Li et al. investigated the coordination relationship between built environment and traffic levels, the results show that the distance from the city center and bus stop have the greatest influence on the coordination relationship [10]. Zheng et al. conducted an interesting study in which they predicted adjacent road traffic based on the occupancy rate of a large office building and achieved good results, which indirectly indicates that the built environment affects the traffic status of adjacent roads [11]. ...
Road traffic congestion is a common problem in most large cities, and exploring the root causes is essential to alleviate traffic congestion. Travel behavior is closely related to the built environment, and affects road travel speed. This paper investigated the direct effect of built environment on the average travel speed of road traffic. Taxi trajectories were divided into 30 min time slot (48 time slots throughout the day) and matched to the road network to obtain the average travel speed of road segments. The Points of Interest (POIs) in the buffer zone on both sides of the road segment were used to calculate the built environment indicators corresponding to the road segment, and then a spatial panel data model was proposed to assess the influence of the built environment adjacent to the road segment on the average travel speed of the road segment. The results demonstrated that the bus stop density, healthcare service density, sports and leisure service density, and parking entrance and exit density are the key factors that positively affect the average road travel speed. The residential community density and business building density are the key factors that negatively affect the average travel speed. Built environments have spatial correlation and spatial heterogeneity in their influence on the average travel speed of road segments. Findings of this study may provide useful insights for understanding the correlation between road travel speed and built environment, which would have important implications for urban planning and governance, traffic demand forecasting and traffic system optimization.
