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Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy the limited curb space that was designed and built decades ago. These PUDOs could jam curbside utilization and disturb the mainline traffic flow, evidently leading to significant negative societal externalities. However, there is a lack of an analytical framework that rigorously quantifies and mitigates the congestion effect of PUDOs in the system view, particularly with little data support and involvement of confounding effects. To bridge this research gap, this paper develops a rigorous causal inference approach to estimate the congestion effect of PUDOs on general regional networks. A causal graph is set to represent the spatiotemporal relationship between PUDOs and traffic speed, and a double and separated machine learning (DSML) method is proposed to quantify how PUDOs affect traffic congestion. Additionally, a rerouting formulation is developed and solved to encourage passenger walking and traffic flow rerouting to achieve system optimization. Numerical experiments are conducted using real-world data in the Manhattan area. On average, 100 additional units of PUDOs in a region could reduce the traffic speed by 3.70 and 4.54 miles/hour (mph) on weekdays and weekends, respectively. Rerouting trips with PUDOs on curb space could respectively reduce the system-wide total travel time (TTT) by 2.44% and 2.12% in Midtown and Central Park on weekdays. A sensitivity analysis is also conducted to demonstrate the effectiveness and robustness of the proposed framework.

Funding: The work described in this paper was supported by the National Natural Science Foundation of China [Grant 52102385], grants from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grants PolyU/25209221 and PolyU/15206322], a grant from the Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) at the Hong Kong Polytechnic University [Grant P0043552], and a grant from Hong Kong Polytechnic University [Grant P0033933]. S. Qian was supported by a National Science Foundation Grant [Grant CMMI-1931827].

Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0195 .

 

Abstract Antarctic Bottom Water is primarily formed via overflows of dense shelf water (DSW) around the Antarctic continental margins. The dynamics of these overflows therefore influence the global abyssal stratification and circulation. Previous studies indicate that dense overflows can be unstable, energizing topographic Rossby waves (TRW) over the continental slope. However, it remains unclear how the wavelength and frequency of the TRWs are related to the properties of the overflowing DSW and other environmental conditions, and how the TRW properties influence the downslope transport of DSW. This study uses idealized high-resolution numerical simulations to investigate the dynamics of overflow-forced TRWs and the associated downslope transport of DSW. It is shown that the propagation of TRWs is constrained by the geostrophic along-slope flow speed of the DSW and by the dynamics of linear plane waves, allowing the wavelength and frequency of the waves to be predicted a priori. The rate of downslope DSW transport depends nonmonotonically on the slope steepness: steep slopes approximately suppress TRW formation, resulting in steady, frictionally dominated DSW descent. For slopes of intermediate steepness, the overflow becomes unstable and generates TRWs, accompanied by interfacial form stresses that drive DSW downslope relatively rapidly. For gentle slopes, the TRWs lead to the formation of coherent eddies that inhibit downslope DSW transport. These findings may explain the variable properties of TRWs observed in oceanic overflows, and they imply that the rate at which DSW descends to the abyssal ocean depends sensitively on the manifestation of TRWs and/or nonlinear eddies over the continental slope. 

A bstract We present the first complete next-to-leading-order (NLO) prediction with full jet algorithm implementation for the single inclusive jet production in pA collisions at forward rapidities within the color glass condensate (CGC) effective theory. Our prediction is fully differential over the final state physical kinematics, which allows the implementation of any infra-red safe observable including the jet clustering procedure. The NLO calculation is organized with the aid of the observable originated power counting proposed in [1] which gives rise to the novel soft contributions in the CGC factorization. We achieve the fully-differential calculation by constructing suitable subtraction terms to handle the singularities in the real corrections. The subtraction contributions can be exactly integrated analytically. We present the NLO cross section with the jets constructed using the anti- k T algorithm. The NLO calculation demonstrates explicitly the validity of the CGC factorization in jet production. Furthermore, as a byproduct of the subtraction method, we also derive the fully analytic cross section for the forward jet production in the small- R limit. We show that in the small- R approximation, the forward jet cross section can be factorized into a semi-hard cross section that produces a parton and the semi-inclusive jet functions (siJFs). We argue that this feature holds for generic jet production and jet substructure observables in the CGC framework. Last, we show numerical analyses of the derived formula to validate our calculations. We justify when the small- R approximation is appropriate. Like forward hadron production, the obtained NLO result also exhibits the negativity of the cross section in the large jet transverse regime, which signals the need for the threshold resummation. A sketch of the threshold resummation in the CGC framework is presented based on the multiple emission picture and it is found to agree with the approach using the rapidity renormalization group equation developed in [2]. 

A bstract We present results for the soft drop groomed jet radius R g at next-to-leading logarithmic accuracy. The radius of a groomed jet which corresponds to the angle between the two branches passing the soft drop criterion is one of the characteristic observables relevant for the precise understanding of groomed jet substructure. We establish a factorization formalism that allows for the resummation of all relevant large logarithms, which is based on demonstrating the all order equivalence to a jet veto in the region between the boundaries of the groomed and ungroomed jet. Non-global logarithms including clustering effects due to the Cambridge/Aachen algorithm are resummed to all orders using a suitable Monte Carlo algorithm. We perform numerical calculations and find a very good agreement with Pythia 8 simulations. We provide theoretical predictions for the LHC and RHIC.