Beyond the dichotomy: How ride-hailing competes with and complements public transport

Since ride-hailing has become an important travel alternative in many cities worldwide, a fervent debate is underway on whether it competes with or complements public transport services. We use Uber trip data in six cities in the United States and Europe to identify the most attractive public transport alternative for each ride. We then address the following questions: (i) How does ride-hailing travel time and cost compare to the fastest public transport alternative? (ii) What proportion of ride-hailing trips do not have a viable public transport alternative? (iii) How does ride-hailing change overall service accessibility? (iv) What is the relation between demand share and relative competition between the two alternatives? Our findings suggest that the dichotomy—competing with or complementing—is false. Though the vast majority of ride-hailing trips have a viable public transport alternative, between 20% and 40% of them have no viable public transport alternative. The increased service accessibility attributed to the inclusion of ride-hailing is greater in our US cities than in their European counterparts. Demand split is directly related to the relative competitiveness of travel times i.e. when public transport travel times are competitive ride-hailing demand share is low and vice-versa.

Risk-Adjusted Mortality Rates as a Quality Proxy Outperform Volume in Surgical Oncology—A New Perspective on Hospital Centralization Using National Population-Based Data

PURPOSE Despite a long-known association between annual hospital volume and outcome, little progress has been made in shifting high-risk surgery to safer hospitals. This study investigates whether the risk-standardized mortality rate (RSMR) could serve as a stronger proxy for surgical quality than volume. METHODS We included all patients who underwent complex oncologic surgeries in Germany between 2010 and 2018 for any of five major cancer types, splitting the data into training (2010-2015) and validation sets (2016-2018). For each surgical group, we calculated annual volume and RSMR quintiles in the training set and applied these thresholds to the validation set. We studied the overlap between the two systems, modeled a market exit of low-performing hospitals, and compared effectiveness and efficiency of volume- and RSMR-based rankings. We compared travel distance or time that would be required to reallocate patients to the nearest hospital with low-mortality ranking for the specific procedure. RESULTS Between 2016 and 2018, 158,079 patients were treated in 974 hospitals. At least 50% of high-volume hospitals were not ranked in the low-mortality group according to RSMR grouping. In an RSMR centralization model, an average of 32 patients undergoing complex oncologic surgery would need to relocate to a low-mortality hospital to save one life, whereas 47 would need to relocate to a high-volume hospital. Mean difference in travel times between the nearest hospital to the hospital that performed surgery ranged from 10 minutes for colorectal cancer to 24 minutes for pancreatic cancer. Centralization on the basis of RSMR compared with volume would ensure lower median travel times for all cancer types, and these times would be lower than those observed. CONCLUSION RSMR is a promising proxy for measuring surgical quality. It outperforms volume in effectiveness, efficiency, and hospital availability for patients.

The New Approaches Based on Ant Colony Optimization and Greedy Search Algorithm for Solving Hierarchical Chinese Postman Problem with Stochastic Travel Times

Hierarchical Chinese postman problem (HCPP), a variant of the Chinese postman problem, aims to find the shortest tour or tours by passing through the arcs classified according to precedence relationship. HCPP, which has a wide application area in real-life problems such as shovel snow and routing patrol vehicles where precedence relations are important, belongs to the NP-hard problem class. In real-life problems, travel time between the two locations in city traffic varies due to reasons such as traffic jam, weather conditions, etc. Therefore travel times are uncertain. In this study, HCPP is handled with the chance-constrained stochastic programming approach, and a new type of problem, hierarchical Chinese postman problem with stochastic travel times, is introduced. Due to the NP-hard nature of the problem, the developed mathematical model with stochastic parameter values cannot find proper solutions in large size problems within the appropriate time interval. Therefore, two new solution approaches, a heuristic method based on the Greedy Search (GSA) algorithm and a meta-heuristic method based on ant colony optimization (ACO) are proposed in this study. These new algorithms were tested on modified benchmark instances and randomly generated problem instances with as many as 817 edges. The performance of algorithms was compared in terms of solution quality and computational time.

Travelling Salesman Problem Generalized Interval Arithmetic

The travelling salesman problem is one of the famous combinatorial optimization problem and has been intensively studied in the last decades. We present a new extension of the basics problem, where travel times are specified as a range of possible values. Keywords: Fuzzy sets, Arithmetic operation on interval, least common method, travelling salesman problem.

Modelling STEMI service delivery: a proof of concept study

BackgroundAccess to individual percutaneous coronary intervention (PCI) centres has traditionally been determined by historical referral patterns along arbitrarily defined geographic boundaries. We set out to produce predictive models of ST-elevation myocardial infarction (STEMI) demand and time-efficient access to PCI centres.MethodsTravel times from random addresses to PCI centres in Melbourne, Australia, were estimated using Google map application programming interface (API). Departures at 08:15 and 17:15 were compared with 23:00 to determine the effect of peak hour traffic congestion. Real-world ambulance travel times were compared with estimated travel times using Google map developer software. STEMI incidence per postcode was estimated by merging STEMI incidence per age group data with age group per postcode census data. PCI centre network configuration changes were assessed for their effect on hospital STEMI loading, catchment size, travel times and the number of STEMI cases within 30 min of a PCI centre.ResultsNearly 10% of STEMI cases travelled more than 30 min to a PCI centre, increasing to 20% by modelling the removal of large outer metropolitan PCI centres (p<0.05). A model of 7 PCI centres compared favourably to the current existing network of 11 PCI centres (p=0.18 (afternoon), p=0.5 (morning and night)). The intraclass correlation between estimated travel times and ambulance travel times was 0.82, p<0.001.ConclusionThis paper provides a framework to integrate prehospital environmental variables, existing or altered healthcare resources and health statistics to objectively model STEMI demand and consequent access to PCI. Our methodology can be modified to incorporate other inputs to compute optimum healthcare efficiencies.