Efficient airport airside ground movement(AAGM)is key to successful operations of urban air mobility.Recent studies have introduced the use of multi-objective multigraphs(MOMGs)as the conceptual prototype to formulate...Efficient airport airside ground movement(AAGM)is key to successful operations of urban air mobility.Recent studies have introduced the use of multi-objective multigraphs(MOMGs)as the conceptual prototype to formulate AAGM.Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs,however,previous work chiefly focused on single-objective simple graphs(SOSGs),treated cost enquires as search problems,and failed to keep a low level of computational time and storage complexity.This paper concentrates on the conceptual prototype MOMG,and investigates its node feature extraction,which lays the foundation for efficient prediction of shortest path costs.Two extraction methods are implemented and compared:a statistics-based method that summarises 22 node physical patterns from graph theory principles,and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space.The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction,while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs.Three regression models are applied to predict the shortest path costs to demonstrate the performance of each.Our experiments on randomly generated benchmark MOMGs show that(i)the statistics-based method underperforms on characterising small distance values due to severe overestimation;(ii)A subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns;and(iii)the learning-based method consistently outperforms the statistics-based method,while maintaining a competitive level of computational complexity.展开更多
Path marginal cost （PMC） is the change in totaltravel cost for flow on the network that arises when timedependentpath flow changes by 1 unit. Because it is hardto obtain the marginal cost on all the links, the local...Path marginal cost （PMC） is the change in totaltravel cost for flow on the network that arises when timedependentpath flow changes by 1 unit. Because it is hardto obtain the marginal cost on all the links, the local PMC,considering marginal cost of partial links, is normallycalculated to approximate the global PMC. When analyzingthe marginal cost at a congested diverge intersection, ajump-point phenomenon may occur. It manifests as alikelihood that a vehicle may unsteadily lift up （down） inthe cumulative flow curve of the downstream links. Previously,the jump-point caused delay was ignored whencalculating the local PMC. This article proposes an analyticalmethod to solve this delay which can contribute toobtaining a more accurate local PMC. Next to that, we usea simple case to calculate the previously local PMC and themodified one. The test shows a large gap between them,which means that this delay should not be omitted in thelocal PMC calculation.展开更多
Geospatial technology is a useful tool when identifying land corridors for transportation networks. The primary transit corridor between Los Angeles, CA and Las Vegas, NV is Interstate-15, approximately a four-hour au...Geospatial technology is a useful tool when identifying land corridors for transportation networks. The primary transit corridor between Los Angeles, CA and Las Vegas, NV is Interstate-15, approximately a four-hour automobile trip without traffic. Virgin Trains USA LLC proposes an alternative means of travel by constructing a high-speed railway along Interstate-15 connecting Las Vegas and Victorville, CA. This study uses least-cost path analysis to propose an optimized alternative corridor for Virgin Trains’ proposed high-speed railway through a system facilitated road and rail accessibility analysis. Previous research using least-cost path and accessibility methodologies evaluated the results of proposed high-speed railway corridors and the system facilitated accessibility changes by visually inspecting deviations from a planned corridor using single or multiple cost criteria as inputs for a weighted cost surface. However, robust analyses of previous least-cost path studies’ corridors are lacking. This proof-in-concept study proposes a less costly corridor through least-cost path analysis and measures the social impact on the stakeholders of a high-speed railway transportation system through system facilitated accessibility. This study’s proposed alternative corridor is 31% shorter than Virgin Trains’ planned corridor and system facilitated accessibility to Las Vegas, NV is increased in 99.74% of Los Angeles County’s census tracts. These results support this study’s position that geospatial technology can support transportation planning in a comprehensive method that considers the transportation corridor and benefits its stakeholders.展开更多
基金This work was supported by the UK Engineering and Physical Sciences Research Council(grant no.EP/N029496/1,EP/N029496/2,EP/N029356/1,EP/N029577/1,and EP/N029577/2)the joint scholarship of the China Scholarship Council and Queen Mary,University of London(grant no.202006830015).
文摘Efficient airport airside ground movement(AAGM)is key to successful operations of urban air mobility.Recent studies have introduced the use of multi-objective multigraphs(MOMGs)as the conceptual prototype to formulate AAGM.Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs,however,previous work chiefly focused on single-objective simple graphs(SOSGs),treated cost enquires as search problems,and failed to keep a low level of computational time and storage complexity.This paper concentrates on the conceptual prototype MOMG,and investigates its node feature extraction,which lays the foundation for efficient prediction of shortest path costs.Two extraction methods are implemented and compared:a statistics-based method that summarises 22 node physical patterns from graph theory principles,and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space.The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction,while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs.Three regression models are applied to predict the shortest path costs to demonstrate the performance of each.Our experiments on randomly generated benchmark MOMGs show that(i)the statistics-based method underperforms on characterising small distance values due to severe overestimation;(ii)A subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns;and(iii)the learning-based method consistently outperforms the statistics-based method,while maintaining a competitive level of computational complexity.
文摘Path marginal cost （PMC） is the change in totaltravel cost for flow on the network that arises when timedependentpath flow changes by 1 unit. Because it is hardto obtain the marginal cost on all the links, the local PMC,considering marginal cost of partial links, is normallycalculated to approximate the global PMC. When analyzingthe marginal cost at a congested diverge intersection, ajump-point phenomenon may occur. It manifests as alikelihood that a vehicle may unsteadily lift up （down） inthe cumulative flow curve of the downstream links. Previously,the jump-point caused delay was ignored whencalculating the local PMC. This article proposes an analyticalmethod to solve this delay which can contribute toobtaining a more accurate local PMC. Next to that, we usea simple case to calculate the previously local PMC and themodified one. The test shows a large gap between them,which means that this delay should not be omitted in thelocal PMC calculation.
文摘Geospatial technology is a useful tool when identifying land corridors for transportation networks. The primary transit corridor between Los Angeles, CA and Las Vegas, NV is Interstate-15, approximately a four-hour automobile trip without traffic. Virgin Trains USA LLC proposes an alternative means of travel by constructing a high-speed railway along Interstate-15 connecting Las Vegas and Victorville, CA. This study uses least-cost path analysis to propose an optimized alternative corridor for Virgin Trains’ proposed high-speed railway through a system facilitated road and rail accessibility analysis. Previous research using least-cost path and accessibility methodologies evaluated the results of proposed high-speed railway corridors and the system facilitated accessibility changes by visually inspecting deviations from a planned corridor using single or multiple cost criteria as inputs for a weighted cost surface. However, robust analyses of previous least-cost path studies’ corridors are lacking. This proof-in-concept study proposes a less costly corridor through least-cost path analysis and measures the social impact on the stakeholders of a high-speed railway transportation system through system facilitated accessibility. This study’s proposed alternative corridor is 31% shorter than Virgin Trains’ planned corridor and system facilitated accessibility to Las Vegas, NV is increased in 99.74% of Los Angeles County’s census tracts. These results support this study’s position that geospatial technology can support transportation planning in a comprehensive method that considers the transportation corridor and benefits its stakeholders.
