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Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems’ greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation.

To explore the association between the optimal coagulant for tofu and the components of soybeans,30 different kinds of soybeans were selected, and tested for their optimal coagulant MgCl2 content. The optimal amount of coagulant was taken as the dependent variable, and the soybean Composition were taken as independent variables for the correlation analysis. The results showed that there was a positive correlation between the optimal coagulant content and the content of histidine, 7S β-conglycinin, B1aB1bB2B3B4 of 11 s glycincin, and α'-subunit of 7S β-conglycinin, negative correlation with lysine. The regression formula is y = -1.186 + 3.457*B1aB1bB2B3B4 + 2.304*7S + 0.351*histidine − 0.084*lysine + 4.696*α', and the model is validated to be within 10 % of the error value and has a high degree of confidence. This study provides theoretical support for realizing the green production of traditional soybean products.

Abstract Background At present, the extent and clinical relevance of epigenetic differences between upper tract urothelial carcinoma (UTUC) and urothelial carcinoma of the bladder (UCB) remain largely unknown. Here, we conducted a study to describe the global DNA methylation landscape of UTUC and UCB and to address the prognostic value of DNA methylation subtype and responses to the DNA methyltransferase inhibitor SGI-110 in urothelial carcinoma (UC). Methods Using whole-genome bisulfite sequencing (n = 49 samples), we analyzed epigenomic features and profiles of UTUC (n = 36) and UCB (n = 9). Next, we characterized potential links between DNA methylation, gene expression (n = 9 samples), and clinical outcomes. Then, we integrated an independent UTUC cohort (Fujii et al., n = 86) and UCB cohort (TCGA, n = 411) to validate the prognostic significance. Furthermore, we performed an integrative analysis of genome-wide DNA methylation and gene expression in two UC cell lines following transient DNA methyltransferase inhibitor SGI-110 treatment to identify potential epigenetic driver events that contribute to drug efficacy. Results We showed that UTUC and UCB have very similar DNA methylation profiles. Unsupervised DNA methylation classification identified two epi-clusters, Methy-High and Methy-Low, associated with distinct muscle-invasive statuses and patient outcomes. Methy-High samples were hypermethylated, immune-infiltrated, and enriched for exhausted T cells, with poor clinical outcome. SGI-110 inhibited the migration and invasion of Methy-High UC cell lines (UMUC-3 and T24) by upregulating multiple antitumor immune pathways. Conclusions DNA methylation subtypes pave the way for predicting patient prognosis in UC. Our results provide mechanistic rationale for evaluating SGI-110 in treating UC patients in the clinic.

Abstract Introduction To describe our initial experience with ileal ureteral replacement (IUR) for the management of ureteral avulsion (UA) during ureteroscopic lithotripsy. Methods Between September 2010 and April 2021, ten patients received ileal ureteral replacement for ureteral avulsion during ureteroscopic lithotripsy. Anterograde urography and computed tomography urography (CTU) were applied to evaluate the lesion. Follow-up was performed with magnetic resonance urography and renal ultrasound as well as clinical assessment of symptoms. We retrospectively analysed the clinical data of ten patients treated with ileal ureteral replacement for the treatment of ureteral avulsion. Results Four patients underwent open ileal ureteral replacement, two underwent laparoscopic ileal ureteral replacement, and four underwent robotic-assisted ileal ureteral replacement. The mean operative time (OT) was 310 min (range 191–530). The mean estimated blood loss (EBL) was 193 mL (range 10–1000). The mean length of the ileal graft was 21 cm (range 12–25). The median postoperative hospital time was 13 days (range 7–19). All surgeries were effectively completed, and no case required open conversion in laparoscopic and robotic-assisted surgeries. There was no obvious hydronephrosis according to contrast-enhanced computed tomography 3-dimensional reconstruction images without serious complications or progressive hydronephrosis during a median follow-up duration of 51 months (range 5–131), and the success rate was 100%. Conclusions Our initial results and experience showed that ileal ureteral replacement for the management of ureteral avulsion during ureteroscopic lithotripsy is safe and feasible.

Purpose: To evaluate the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) detection via target biopsy (TB), systematic biopsy (SB), and combined biopsy (CB) in patients with PI-RADS 5 lesions. Methods: Patients with at least one PI-RADS 5 lesion were retrospectively enrolled in a prospectively collected database. The patients underwent multiparametric magnetic resonance imaging (mpMRI) followed by transrectal TB of PI-RADS 5 lesions and SB. The PCa and CSPCa detection rates and cores of TB and SB were compared with those of CB. Results: In 585 patients, prostate biopsy revealed PCa in 560 cases (95.73%) and CSPCa in 549 cases (93.85%). PCa was detected in T2 patients (93.13%, 217/233) and in T3/4 patients (97.44%, 343/352). CSPCa was detected in T2 patients (89.27%, 208/233) and in T3/4 patients (96.87%, 341/352). The positive rates of TB for T2/3/4, T3/4, and T2 were 94.02%, 96.21%, and 90.56%, respectively. SB added 1.71% (10/585) PCa and 1.37% (8/585) CSPCa detection to TB. There was no difference between TB and SB in detecting different stages of cancer (p > 0.05). In the biopsy core analysis, TB had fewer biopsy cores and a higher detection rate than SB (all p < 0.05). Conclusions: In patients with PI-RADS score 5 lesions, TB can achieve the same detection rate as, with fewer biopsy cores than, CB. SB adds minimal clinical value and can be omitted for these patients.

BackgroundsDespite the genomic landscape of urothelial carcinomas (UC) patients, especially those with UC of bladder (UCB), has been comprehensively delineated and associated with pathogenetic mechanisms and treatment preferences, the genomic characterization of upper tract UC (UTUC) has yet to be fully elucidated.Materials and methodsA total of 131 Chinese UTUC (74 renal pelvis & 57 ureter) and 118 UCB patients were enrolled in the present study, and targeted next-generation sequencing (NGS) of 618 cancer-associated genes were conducted to exhibit the profile of somatic and germline alterations. The COSMIC database, including 30 mutational signatures, were utilized to evaluate the mutational spectrums. Moreover, TCGA-UCB, MSKCC-UCB, and MSKCC-UTUC datasets were retrieved for preforming genomic alterations (GAs) comparison analysis between Western and Chinese UC patients.ResultsIn our cohort, 93.98% and 56.63% of UC patients were identified with oncogenic and actionable somatic alterations, respectively. Meanwhile, 11.24% of Chinese UC patients (of 14.50% and 7.63% of UTUC and UCB cases, respectively) were identified to harbor a total of 32 pathogenic/likely-pathogenic germline variants in 22 genes, with DNA damage repair (DDR)-associated BRCA1 (1.20%) and CHEK2 (1.20%) being the most prevalent. Chinese UTUC and UCB patients possessed distinct somatic genomic characteristics, especially with significantly different prevalence in KMT2D/C/A, GNAQ, ERCC2, RB1, and PPM1D. In addition, we also found notable differences in the prevalence of ELF3, TP53, PMS2, and FAT4 between renal pelvis and ureter carcinomas. Moreover, 22.90% and 33.90% of UTUC and UCB patients, respectively, had at least one deleterious/likely deleterious alteration in DDR related genes/pathways. Subsequently, mutational signature analysis revealed that UC patients with mutational signature 22, irrespective of UTUC or UCB, consistently had the markedly higher level of tumor mutational burden (TMB), which was proved to be positively correlated with the objective complete/partial response rate in the IMvigor210 cohort. By comparison, Chinese and Western UTUC patients also differed regrading GAs in oncogenic-related genes/pathways, especially in TP53, RTK/RAS, and PI3K pathways; besides, more alterations in WNT pathway but less TP53, RTK/RAS, HIPPO, and PI3K pathways were identified in Chinese UCB.DiscussionsThe in-depth analysis of genomic mutational landscapes revealed distinct pathogenetic mechanisms between Chinese UTUC and UCB, and specific genomic characterizations could identify high risk population of UTUC/UCB and provided information regarding the selection of alternative therapeutic regimens.

When discussing the influence of the built environment on taxi travel demand, few studies have considered the effect of the modifiable areal unit problem (MAUP) or the influence of the "5D" dimensions of the built environment (It refers to the consideration of the built environment from five dimensions of density, diversity, design, destination accessibility and distance to transit.) on taxi travel demand. Moreover, discussion of the nonlinear and linear relationships between taxi demand and environment variables is also lacking. To address these gaps, we constructed a "5D" dimension index system of built environment variables. The influence of the MAUP on the model results was discussed using the optimal parameter-based geographical detector (OPGD) model, and the optimal spatial analysis unit was selected. The OPGD and multiscale geographically weighted regression (MGWR) models were used to reveal the influence of different dimensions of the built environment on taxi travel demand from global and local perspectives, respectively. Finally, the central urban area of Xi'an was analyzed as an example. The results show the following: (1) Most built environment variables are sensitive to the influence of MAUP. (2) It is better to divide the space into regular hexagons than squares, and the optimal spatial analysis unit in this study is a regular hexagon grid with sides of 900m. (3) From a global perspective, the distance to the city center, commercial residence POI density, transportation facility POI density, and population density have the greatest influence on the demand for taxi travel. (4) From a local perspective, the MGWR model considering spatial heterogeneity and scale differences is superior to the GWR model, and the influence of built environment variables exhibited spatial heterogeneity. The proposed optimal spatial analysis unit can provide a basis for taxi demand forecasting and scheduling. This study provides a reference for urban planners and traffic managers to offer optimization strategies related to the built environment, promote healthy development of the taxi industry, and solve the problems of the urban transportation system.

Taxis provide essential transport services in urban areas. In the taxi industry, the income level remains a cause of concern for taxi drivers as well as regulators. Analyzing the variation trend of taxi operation efficiency indicators throughout the day, mining high-income orders hot-spots and high-income regions at different periods, will effectively improve the average hourly incomes (AHI) of drivers. This paper selects the order data for each day of holidays, working days, and non-working days through the taxi order dataset of October 2019 in Xi'an. Firstly, we analyze the variation trend of taxi operation efficiency indicators in the three days. We next divide the orders into four income levels based on the Natural Breaks accordingly. Then, we use Tyson polygon and mash map matching methods to visualize the high-income orders hot-spots and high-income regions. It is significantly to analyze and summarize the visualization results. Finally, we compute the Moran'I index to measure the spatial correlation between high-income orders regions and high-income regions. The results show that (1) the number and the spatial distribution of high-income orders hot-spots and high-income regions at different periods are different. (2) Some places are hot-spots, but neither high-income orders hot-spots nor high-income regions. (3) The high-income orders regions and high-income regions have a strong correlation in spatial distribution. This study provides suggestions and insights to taxi companies and taxi drivers to increase their average hourly income (AHI) and enhance the efficiency of the taxi industry.

ObjectivesTo summarize the clinicopathological diagnostic features and evolutionary trends of upper tract urothelial carcinoma (UTUC) in China over the past 20 years.MethodsAll patients diagnosed with upper tract urothelial carcinoma in the Peking University First Hospital from 2001 to 2020 were retrospectively collected. Data were divided into two groups (2001-2010 and 2011-2020) according to the date of diagnosis. Statistical analysis was done with the SPSS V22.0. Chi-square analysis and t-test were adopted to analyze depending on the data type. Subgroup analysis based on 5 years was used for visualization to present trends. Both Kaplan-Meier curve and Cox regression were used for univariate and multivariate survival analysis.ResultsThe study included 2561 cases diagnosed with upper tract urothelial carcinoma in total. Compared with the first decade (2001-2010), patients of the second decades (2011-2020) had elder mean age (66.65 versus 67.59, years, p=0.025), higher male proportion (43.5% versus 49.0%, p=0.034), lower incidence of renal pelvic tumors (53.4% versus 45.8%, p

The roadside deployed light detecting and ranging (LiDAR) has been a solution to fill the data gap for the transition period from the unconnected-vehicles environment to the connected-vehicles system. For the roadside LiDAR system, background filtering is an initial but important step. This paper presented a raster-based method for background filtering with roadside LiDAR data. The proposed method contains four major parts: region of interest (ROI) selection, rasterization, background area detection, and background array generation. The location of the background points was stored in a 3D array. The performance of the raster-based method was tested with the data collected at different scenarios. The comparison to the state-of-the-art also confirmed the robustness of the proposed method.

Deer crossing roads are a major concern of highway safety in rural and suburban areas in the United States. This paper provided an innovative approach to detecting deer crossing at highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, and object classification. An automatic background filtering method based on the point distribution was applied to exclude background but keep the deer (and road users if they exist) in the space. A modified density-based spatial clustering of applications with noise (DBSCAN) algorithm was used for object clustering. Adaptive searching parameters were applied in the vertical and horizontal directions to cluster the points. The cluster groups were further classified into three groups - deer, pedestrians, and vehicles, using three different algorithms: naive Bayes, random forest, and k-nearest neighbor. The testing results showed that the random forest (RF) can provide the highest accuracy for classification among the three algorithms. The results of the field test showed that the developed method can detect the deer with an average distance of 30 m far away from the LiDAR. The time delay is about 0.2 s in this test. The deer crossing information can warn drivers about the risks of deer-vehicle crashes in real time.

Connected-vehicle system is an important component of smart cities. The complete benefits of connected-vehicle technologies need the real-time information of all vehicles and other road users. However, the existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic since there are still many unconnected vehicles and pedestrians on the roads. Therefore, it is urgent to find an approach to collect the high-resolution real-time status of unconnected road users. When it is difficult for all vehicles, pedestrians, and bicyclists to broadcast their real-time status in the near future, enhancing the traffic infrastructures to actively sense and broadcast each road user's status is an intuitive solution to fill the data gap. This paper introduces a new-generation LiDAR-enhanced connected infrastructures that can actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The system architecture, the LiDAR data processing procedure, the data communication, and the first pilot implementation at an intersection in Reno, Nevada are included in this paper. This research is the start of the new-generation connected infrastructures serving connected/autonomous vehicles with the roadside LiDAR sensors. It will accelerate the deployment of the connected network for the smart cities to improve traffic safety, mobility, and fuel efficiency.

The existing rectangular rapid flashing beacons (RRFB) could not be automatically trigged with acceptable accuracy. This paper introduces a strategy to develop an automatic trigged RRFB system using the roadside Light Detection and Ranging (LiDAR). The trajectories of the road users can be extracted in real-time with the proposed LiDAR data processing algorithms. The pedestrian/bicycle crossing intention was predicted with Naïve Bayes (NB) classifier. Different detection zones were provided to detect the pedestrian/bicycle crossing movements in real-time. The updated RRFB system was implemented at one intersection in Henderson, Nevada, United States. The results of the field test showed that the overall false rate of the updated RRFB system is very low (0.68%). The performance of the proposed method is superior compared to the state-of-the-art method. The updated RRFB system can greatly improve pedestrian safety at unsignalized intersections.

Over the past few decades, taxi drivers’ income has received extensive attention from scholars. Previous studies have investigated the factors affecting taxi drivers’ income from multiple perspectives. However, less attention has been paid to road network topology, which has a direct impact on taxis’ operation efficiency and drivers’ income. To fill this gap, this paper examines the relationship between taxi drivers’ income and urban road network topology; we employed various methods, namely, spatial design network analysis (sDNA), bivariate Moran’s I, and geographically weighted regression (GWR). The results show the following. (1) The total order income (TOI) of taxi drivers has a certain degree of positive spatial correlation with closeness and betweenness. (2) The impact of urban road network topology on the average order income (AOI) of taxi drivers is stable. Specifically, closeness and betweenness have significant impacts on the AOI of taxi drivers at the medium and larger scales. (3) Closeness has a negative impact on the AOI of taxi drivers, and betweenness has a positive impact on the AOI of taxi drivers. (4) Compared with betweenness, the impact of closeness on the AOI of taxi drivers is greater and more stable. These findings can provide useful reference values for the development of policies aimed at improving both taxi drivers’ income and urban road network efficiency.

Background: Vertebral arteriovenous fistula (AVF) associated with neurofibromatosis type 1 (NF-1) is a rare condition in the previous reports. However, whether vertebral AVF in NF-1 is congenital or NF-1 disease progression hasn't been clarified.Case Description: We reported a 48-year-old male case of vertebral AVF simultaneously combined with thoracic scoliosis and NF-1. Preoperative CT angiography showed the AVF with multiple orifices located on the vessel wall of the vertebral artery, which was proved during the procedure of endovascular treatment. By occluding the parent vertebral artery, the AVF was finally cured. Further whole-exome sequencing identified a novel germline heterozygous point nonsense mutation, c.G397T(p.E133X), in the NF1(NM_000267) gene exon4.Conclusions: From this patient, we speculate that vertebral AVF associated with NF-1 might be a congenital disease as a manifestation of mesodermal dysplasia.

As an important service industry in cities, taxis provide people with an all-weather travel mode. And its demand is greatly affected by the internal functions of the city. It is very important to understand the relationship between the mixed degree of urban internal functions and the residents' taxi travel demand to alleviate traffic congestion and formulate corresponding urban traffic strategies. This paper combined two heterogeneous data in the main urban area of Xi'an, urban points of interest (POIs) and taxi GPS. Firstly, a spatial information entropy model was constructed to quantitatively evaluate the mixed degree of functions in different spaces within the city. Secondly, the kernel density estimation method was used to analyze the spatial distribution evolution characteristics of residents' taxi travel demand. A geographically weighted regression (GWR) model was further used to study the spatial and temporal influences of the mixed degree of urban internal functions on taxi travel demand. Results indicate that there is an obvious spatiotemporal pattern in the impact of the mixed degree of urban functions on taxi travel demand. And the GWR model is used to study the impact is superior to the ordinary least squares (OLS). In more developed areas, improving the mixed degree of urban functions will be more attractive than backward areas. It is also found that although the single function of the city has an impact on the taxi travel demand, the result of the single function is not ideal. This study can provide a reference for the optimal combination of basic units of urban space in urban planning, promote the balance of supply and demand of urban taxis, rationalize urban taxis' operation and allocation, and solve the problems of urban transportation systems.

This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium state; thus, a link-based day-to-day traffic assignment model is employed to compute TSTT and simulate the traffic evolution. Two indicators are developed to assess the road network resilience, i.e., the resilience of performance loss and the resilience of recovery rapidity. The former is calculated based on TSTT, and the latter is computed according to the restoration makespan. Then, we formulate the restoration optimization problem as a resilience-based bi-objective mixed integer programming model aiming to maximize the network resilience. Due to the NP-hardness of the model, a genetic algorithm is developed to solve the model. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The effects of key parameters including the number of work crews, travelers’ sensitivity to travel time, availability of budget, and decision makers’ preference on the values of the two objectives are investigated as well.

Background and Aims: Cognitive impairment is one of the major complications of subarachnoid hemorrhage (SAH) and is closely associated with neuroinflammation. Hydrogen sulfide (H2S) has been shown to have an anti-inflammatory effect and reduce cognitive impairment in neurodegenerative diseases, but its effects in SAH have been little studied. This study aimed to investigate the effects of H2S on cognitive impairment after SAH and the possible underlying mechanisms.Methods: Forty-eight male Sprague–Dawley (SD) rats were randomly divided into three groups: a sham group, a SAH group, and a SAH + NaHS (an H2S donor) group. The endovascular perforation technique was used to establish the experimental SAH model. NaHS was administered intraperitoneally. An active avoidance test (AAT) was performed to investigate cognitive function. The expression of TNF-α, toll-like receptor 4 (TLR4), and NF-κB p65 in the hippocampus was measured by Western blot and immunohistochemistry. The types of cells expressing TNF-α were detected by double immunofluorescence staining.Results: Compared to that in the sham group, the learning and memory ability of rats in the SAH group was damaged. Furthermore, the expression of TNF-α, TLR4, and NF-κB p65 in the hippocampus was elevated in the SAH group (p < 0.05). TNF-α was mainly expressed in activated microglia, which was consistent with the expression of TLR4. Treatment with NaHS significantly decreased the cognitive impairment of rats after SAH and simultaneously reduced the expression of TNF-α, TLR4, and NF-κB p65 and alleviated the nuclear translocation of NF-κB p65 (p < 0.05).Conclusions: The neuroinflammation reaction in microglia contributes to cognitive impairment after SAH. H2S reduced the cognitive impairment of rats after SAH by ameliorating neuroinflammation in microglia, potentially via the TLR4/NF-κB pathway.

Low-grade gliomas (LGGs) are the most common CNS tumors, and the main therapy for LGGs is complete surgical resection, due to its curative effect. However, LGG recurrence occurs frequently. Biomarkers play a crucial role in evaluating the recurrence and prognosis of LGGs. Numerous studies have focused on LGG prognosis. However, the multiomics research investigating the roles played by gene methylation and expression in LGG recurrence remains limited. In this study, we integrated the TCGA and GEO datasets, analyzing RNA and methylation data for recurrence (R) and nonrecurrence (NR) groups. We found a low expression of TLX1NB and high methylation in recurrence patients. Low expression of TLX1NB is associated with poor survival (OS: p=0.04). The expression of TLX1NB is likely to play a role in the prognosis of LGG. Therefore, TLX1NB may represent an alternative early biomarker for the recurrence of low-grade gliomas.

An optimal restoration strategy for supply chain networks can efficiently schedule the repair activities under resource limits. However, a wide range of previous studies solve this problem from the perspective of cost-effectiveness instead of a resilient manner. This research formulates the problem as a network maximum-resilience decision. We develop two metrics to measure the resilience of the supply chain networks, i.e., the resilience of cumulative performance loss and the resilience of restoration rapidity. Then, we propose a bi-objective nonlinear programming model, which aims to maximize the network resilience under the budget and manpower constraints. A modified simulated annealing algorithm is employed to solve the model. Finally, a testing supply chain network is utilized to illustrate the effectiveness of the proposed method framework. The results show that the optimal restoration schedule generated by the proposed model is a tradeoff between the cumulative performance loss and the restoration rapidity. Additionally, the sensitivity analysis of parameters indicates that decision-maker’s preference, tolerance factor of delivery time, number of work crews, and availability of budget all have significant impacts on the restoration schedule.

An optimal maintenance scheduling strategy for bridge networks can generate an efficient allocation of resources with budget limits and mitigate the perturbations caused by maintenance activities to the traffic flows. This research formulates the optimal maintenance scheduling problem as a bi-level programming model. The upper-level model is a multi-objective nonlinear programming model, which minimizes the total traffic delays during the maintenance period and maximizes the number of bridges to be maintained subject to the budget limit and the number of crews. In the lower-level, the users’ route choice following the upper-level decision is simulated using a modified user equilibrium model. Then, the proposed bi-level model is transformed into an equivalent single-level model that is solved by the simulated annealing algorithm. Finally, the model and algorithm are tested using a highway bridge network. The results show that the proposed method has an advantage in saving maintenance costs, reducing traffic delays, minimizing makespan compared with two empirical maintenance strategies. The sensitivity analysis reveals that traffic demand, number of crews, availability of budget, and decision maker’s preference all have significant effects on the optimal maintenance scheduling scheme for bridges including time sequence and job sequence.

Traffic congestion is a significant problem in many major cities. Getting stuck in traffic, the mileage per unit time that a taxicab travels will decline significantly. Congestion premium (or so-called low-speed fare) has become an increasingly important income source for taxi drivers. However, the impact of congestion premium on the taxicab market is not widely understood yet. In particular, modeling and analyzing of the taxi fare structure with congestion premium are extremely limited. In this paper, we developed a taxi price equilibrium model, in which the adjustment mechanism of congestion premium on optimizing the taxi driver’s income, balancing the supply and demand, and eventually improving the level of service in the whole taxicab market was investigated. In the final part, we provided a case study to demonstrate the feasibility of the proposed model. The results indicated that the current taxi fare scheme in Beijing is suboptimal, since the gain from the raise of congestion premium cannot compensate for the loss from the demand reduction. Conversely, the optimal fare scheme suggested by our model can effectively reduce the excessive demand and reach the supply-demand equilibrium, while keeping the stability of the driver’s income to the maximum extent.

The Light Detection and Ranging (LiDAR) sensors are being considered as new traffic infrastructure sensors to detect road users’ trajectories for connected/autonomous vehicles and other traffic engineering applications. A LiDAR-enhanced traffic infrastructure system requires multiple LiDAR sensors around intersections, along with road segments, which can provide a seamless detection range at intersections or along arterials. Each LiDAR sensor generates cloud points of surrounding objects in a local coordinate system with the sensor at the origin, so it is necessary to integrate multiple roadside LiDAR sensors’ data into the same coordinate system. None of existing methods can integrate the data from roadside LiDAR sensors, because the extensive detection range of roadside sensors generates low-density cloud points and the alignment of roadside sensors is different from mapping scans or autonomous sensing systems. This paper presents a method to register datasets from multiple roadside LiDAR sensors. This approach innovatively integrates LiDAR datasets with 3D cloud points of road surface and 2D reference point features, so the method is abbreviated as RGP (Registration with Ground and Points). The RGP method applies optimization algorithms to identify the optimized linear coordinate transformation. This research considered the genetic algorithm (global optimization) and the hill climbing algorithm (local optimization). The performance of the RGP method and the different optimization algorithms was evaluated with field LiDAR sensors data. When the developed process can integrate data from roadside sensors, it can also register LiDAR sensors’ data on an autonomous vehicle or a robot.