Cross Comparison of Spatial Partitioning Methods for an Urban Transportation Network

Dantsuji, Takao; Hirabayashi, Shin; Ge, Qian; Fukuda, Daisuke

doi:10.1007/s13177-019-00209-x

Cited by 6 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, by controlling constraint parameters, larger sub-areas are partitioned while smaller sub-areas are reassigned to surrounding sub-areas, reducing the variability in sub-area size. This paper differs from previous literature by not only obtaining line sub-areas through road network partitioning [2], but also deriving smooth surface sub-areas. By overlaying spatial data such as population, economy, or points of interest (POI) [26] onto these surface sub-areas, we can provide valuable theoretical support for traffic partition management and control.…”

Section: Introductionmentioning

confidence: 91%

“…Furthermore, it is important to note that actual road segments are bidirectional and exhibit different data characteristics. Thus, in contrast to previous studies that primarily focused on unidirectional road networks [2,3], our research investigates bidirectional road networks with unidirectional road segments as the minimum unit of analysis.…”

Section: Introductionmentioning

confidence: 99%

“…One limitation of clustering algorithms is the need for customizing the number of sub-areas, without a clear connection to the quality of the partitioning results. Compared to the aforementioned algorithms, community detection algorithms are more suitable for large networks and offer potential advantages in road network partitioning studies [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Road Network Partitioning Considering the Coupling of Network Connectivity and Traffic Attributes

Qin

et al. 2023

IJGI

View full text Add to dashboard Cite

The urban road network is a large and complex system characterized by significant heterogeneity arising from different spatial structures and traffic demands. To facilitate effective management and control, it is necessary to partition the road network into homogeneous sub-areas. In this regard, we aim to propose a hybrid method for partitioning sub-areas with intra-area homogeneity, inter-area heterogeneity, and similar sizes, called CSDRA. It is specifically designed for bidirectional road networks with segment weights that encompass traffic flow, speed, or roadside facility evaluation. Based on community detection and spectral clustering, this proposed method comprises four main modules: initial partition, partitioning of large sub-areas, reassignment of small sub-areas, and boundary adjustment. In the preliminary partitioning work, we also design a road network reconstruction method which further helps to enhance the intra-area homogeneity and inter-area heterogeneity of partitioning results. Furthermore, to align with the requirement for comparable work units in practical traffic management and control, we control the similarity in the size of sub-areas by enforcing upper and lower bound constraints on the size of the sub-areas. We verify the outperformance of the proposed method by an experiment on the partitioning of an urban road network in Guangzhou, China, where we employ sidewalk barrier-free score data as segment weights. The results demonstrate the effectiveness of both the road network reconstruction method and the CSDRA proposed in this paper, as they significantly improve the partitioning outcomes compared with other methods using different evaluation indicators corresponding to the partitioning objectives. Finally, we investigate the influence of constraint parameters on the evaluation indicator. Our findings indicate that appropriately configuring these constraint parameters can effectively minimize sub-region size variations while having minimal impact on other aspects.

show abstract

Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Road Network Partitioning Considering the Coupling of Network Connectivity and Traffic Attributes

Qin

et al. 2023

IJGI

View full text Add to dashboard Cite

show abstract

“…Note that the weight of link (i, j) on day t is set to zero when there is no link on day t. Because the weights of the links are important, we use the weighted similarity function. This is a Gaussian-type probability density function that is also used for similarity functions to partition transportation road networks into homogenous sub-networks (Ji and Geroliminis, 2012;Dantsuji et al, 2019). The values of the similarity function are between 0 and 1, with higher values representing greater similarity between networks.…”

Section: Recurrence In Mobility Pattern Networkmentioning

confidence: 99%

Understanding Changes in Travel Patterns during the COVID-19 Outbreak in the Three Major Metropolitan Areas of Japan

Dantsuji¹,

Sugishita²,

Fukuda³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Unlike the lockdown measures taken in some countries or cities, the Japanese government declared a "State of Emergency" (SOE) under which people were only requested to reduce their contact with other people by at least 70 %, while some local governments also implemented their own mobility-reduction measures that had no legal basis. The effects of these measures are still unclear. Thus, in this study, we investigate changes in travel patterns in response to the COVID-19 outbreak and related policy measures in Japan using longitudinal aggregated mobile phone data. Specifically, we consider daily travel patterns as networks and analyze their structural changes by applying a framework for analyzing temporal networks used in network science. The cluster analysis with the network similarity measures across different dates showed that there are six main types of mobility patterns in the three major metropolitan areas of Japan: (I) weekends and holidays prior to the COVID-19 outbreak, (II) weekdays prior to the COVID-19 outbreak, (III) weekends and holidays before and after the SOE, (IV) weekdays before and after the SOE, (V) weekends and holidays during the SOE, and (VI) weekdays during the SOE. It was also found that travel patterns might have started to change from March 2020, when most schools were closed, and that the mobility patterns after the SOE returned to those prior to the SOE. Interestingly, we found that after the lifting of the SOE, travel patterns remained similar to those during the SOE for a few days, suggesting the possibility that self-restraint continued after the lifting of the SOE. Moreover, in the case of the Nagoya metropolitan area, we found that people voluntarily changed their travel patterns when the number of cases increased.

show abstract

Flow count data-driven static traffic assignment models through network modularity partitioning

Roocroft,

Punzo,

Ramli

2023

Transportation

View full text Add to dashboard Cite

Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a novel approach to partition road networks through network modularity to produce data-driven static traffic assignment models from loop detector data on large road systems. The use of partitioning allows the estimation of the key model input of Origin–Destination demand matrices from flow counts alone. Previous network tomography-based demand estimation techniques have been limited by the network size. The amount of partitioning changes the Origin–Destination estimation optimisation problems to different levels of computational difficulty. Different approaches to utilising the partitioning were tested, one which degenerated the road network to the scale of the partitions and others which left the network intact. Applied to a subnetwork of England’s Strategic Road Network and other test networks, our results for the degenerate case showed flow and travel time errors are reasonable with a small amount of degeneration. The results for the non-degenerate cases showed that similar errors in model prediction with lower computation requirements can be obtained when using large partitions compared with the non-partitioned case. This work could be used to improve the effectiveness of national road systems planning and infrastructure models.

show abstract

Cross Comparison of Spatial Partitioning Methods for an Urban Transportation Network

Cited by 6 publications

References 27 publications

Research on Road Network Partitioning Considering the Coupling of Network Connectivity and Traffic Attributes

Research on Road Network Partitioning Considering the Coupling of Network Connectivity and Traffic Attributes

Understanding Changes in Travel Patterns during the COVID-19 Outbreak in the Three Major Metropolitan Areas of Japan

Flow count data-driven static traffic assignment models through network modularity partitioning

Contact Info

Product

Resources

About