Prediction of travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.     

Area- and gender-based commuting differentials in India’s largest urban-rural region

In the countries of the Global South such as India, rapid urbanization and the increase in individual motorization may lead to a predominance of unsustainable commuting patterns. However, urbanization also has important positive effects, including the empowerment of women. This paper examines newly released, spatially disaggregated data on home-to-work commuting by non-agricultural workers in the National Capital Region of India. It aims to understand and compare commuting patterns in urban and rural areas, including choice of travel modes, commuting distances, and gender differentials.The results reveal a tendency observable in urban residents to use individual motorized transport more often both for short and for long trips, although the proportion of individual motorization is far from what it is in the industrial world. Rural areas are characterized by the predominance of non-motorized travel modes and a large share of long trips. The mobility gap between men and women does not appear to increase with literacy. In urban areas, women often choose to commute by car rather than using green modes of transportation (especially in higher-income districts). The paper stresses the importance of the area and gender differentials that need to be taken into account when formulating regional transport policies.     

Seasonal and interannual variation of physical and biological processes during 1994–2001 in the Sea of Japan/East Sea: A three-dimensional physical–biogeochemical modeling study

A Pacific basin-wide physical–biogeochemical model has been used to investigate the seasonal and interannual variation of physical and biological fields with analyses focusing on the Sea of Japan/East Sea (JES). The physical model is based on the Regional Ocean Model System (ROMS), and the biogeochemical model is based on the Carbon, Si(OH)₄, Nitrogen Ecosystem (CoSiNE) model. The coupled ROMS–CoSiNE model is forced with the daily air–sea fluxes derived from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis for the period of 1994 to 2001, and the model results are used to evaluate climate impact on nutrient transport in Mixed Layer Depth (MLD) and phytoplankton spring bloom dynamics in the JES.The model reproduces several key features of sea surface temperature (SST) and surface currents, which are consistent with the previous modeling and observational results in the JES. The calculated volume transports through the three major straits show that the Korea Strait (KS) dominates the inflow to the JES with 2.46 Sv annually, and the Tsugaru Strait (TS) and the Soya Strait (SS) are major outflows with 1.85 Sv and 0.64 Sv, respectively. Domain-averaged phytoplankton biomass in the JES reaches its spring peak 1.8 mmol N m^− 3 in May and shows a relatively weak autumn increase in November. Strong summer stratification and intense consumption of nitrate by phytoplankton during the spring result in very low nitrate concentration at the upper layer, which limits phytoplankton growth in the JES during the summer. On the other hand, the higher grazer abundance likely contributes to the strong suppression of phytoplankton biomass after the spring bloom in the JES. The model results show strong interannual variability of SST, nutrients, and phytoplankton biomass with sudden changes in 1998, which correspond to large-scale changes of the Pacific Decadal Oscillation (PDO). Regional comparisons of interannual variations in springtime were made for the southern and northern JES. Variations of nutrients and phytoplankton biomass related to the PDO warm/cold phase changes were detected in both the southern and northern JES, and there were regional differences with respect to the mechanisms and timing. During the warm PDO, the nutrients integrated in the MLD increased in the south and decreased in the north in winter. Conversely, during the cold PDO, the nutrients integrated in the MLD decreased in the south and increased in the north. Wind divergence/convergence likely drives the differences in the southern and northern regions when northerly and northwesterly monsoon dominates in winter in the JES. Subjected to the nutrient change, the growth of phytoplankton biomass appears to be limited neither by nutrient nor by light consistently both in the southern and northern regions. Namely, the JES is at the transition zone of the lower trophic-level ecosystem between light-limited and nutrient-limited zones.     

Carbon dioxide emissions from port container distribution: Spatial characteristics and driving factors

Port carbon dioxide (CO₂) emissions in China have become an ever-increasing public concern due to their significant impacts on human health and the environment. However, existing studies focus mainly on CO₂ emissions from vessels calling at the ports and cargo handling within the ports, paying little attention to the inland distribution networks. To fill this gap, this paper proposes an easily implemented method for calculating CO₂ emissions from port container distribution (PCD) and investigates their spatial characteristics and driving factors. By analyzing 30 container ports in China, the main findings are as follows. First, road transportation is the major contributor of CO₂ emissions from PCD due to the lack of rail and inland water transportation. Second, PCD carbon emissions exhibit significant local spatial clustering. That is, ports with similar geographical locations tend to present a similar pattern of PCD carbon emissions. Third, as suggested by the spatial Durbin model, PCD carbon emissions are negatively determined by local gross domestic product, number of port berths, but are positively determined by local tertiary industry value and highway freight volume, and waterway freight volume in both local and neighboring ports. These results provide empirical insights into cross-port collaboration in reducing PCD carbon emissions.     

Investigating empirical implications of hysteresis in day-to-day travel time variability

Day-to-day travel time variability plays a significant role in travel time reliability. Nowadays, travelers not only seek to minimize their travel time on average, but also value its variation. The variation in the mean and the variance of travel time (across days, for the same departure time) has not been thoroughly investigated. A temporary decrease in capacity (e.g. congestion caused by an active bottleneck) leads to a quite significant difference in the variance of travel time for congestion onset and offset periods. This phenomenon results in hysteresis loops where the departure time periods in congestion offset exhibit a higher travel time variance than the ones in congestion onset with the same mean travel time. The aim of this paper is to identify empirical implications that yield to the hysteresis phenomenon in day-to-day travel times. First, empirical hysteresis loop observations are provided from two different freeway sites. Second, we investigate the potential link with the hysteresis observed in traffic networks on macroscopic fundamental diagram (MFD). Third, we build a piecewise linear function that models the evolution of travel time within the day. This allows us to decompose the problem into its components, e.g. start time of congestion, peak travel time, etc. These components, along with their probability distribution functions, are employed in a Monte Carlo simulation model to investigate their partial effects on the existence of hysteresis. Correlation among critical variables is the most influential factor in this phenomenon, which should be further investigated regarding traffic flow and traffic equilibrium principles.     

二氧化碳人工气腹在安氟醚吸入麻醉中对心率变异性及脑电指标的影响

目的　探讨CO2 人工气腹在安氟醚吸入麻醉中对腹腔镜胆囊切除手术患者心率变异性 (heartratevariability ,HRV)及脑电 (electroencephalogram ,EEG)指标的不同影响。 方法　利用心率变异功率谱分析技术 (HRV PSA)和脑电功率谱分析技术 (EEG PSA)对 2 8例腹腔镜胆囊切除手术患者在麻醉前、麻醉诱导插管后及CO2 人工气腹后HRV指标LF、HF、LF HF、TP及EEG参数BIS、95 %SEF的变化进行了观察。结果　麻醉诱导插管后LF、HF、TP、BIS及95 %SEF均显著降低 ,LF HF未见明显改变 ;CO2 人工气腹后LF、LF HF、TP显著升高 ,HF、BIS及 95 %SEF未见明显改变。结论　CO2 人工气腹使交感神经活性显著升高而对大脑皮层意识活动影响不大     

On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts.     

A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times

This study proposes an approach to modeling the effects of daily roadway conditions on travel time variability using a finite mixture model based on the Gamma–Gamma (GG) distribution. The GG distribution is a compound distribution derived from the product of two Gamma random variates, which represent vehicle-to-vehicle and day-to-day variability, respectively. It provides a systematic way of investigating different variability dimensions reflected in travel time data. To identify the underlying distribution of each type of variability, this study first decomposes a mixture of Gamma–Gamma models into two separate Gamma mixture modeling problems and estimates the respective parameters using the Expectation–Maximization (EM) algorithm. The proposed methodology is demonstrated using simulated vehicle trajectories produced under daily scenarios constructed from historical weather and accident data. The parameter estimation results suggest that day-to-day variability exhibits clear heterogeneity under different weather conditions: clear versus rainy or snowy days, whereas the same weather conditions have little impact on vehicle-to-vehicle variability. Next, a two-component Gamma–Gamma mixture model is specified. The results of the distribution fitting show that the mixture model provides better fits to travel delay observations than the standard (one-component) Gamma–Gamma model. The proposed method, the application of the compound Gamma distribution combined with a mixture modeling approach, provides a powerful and flexible tool to capture not only different types of variability—vehicle-to-vehicle and day-to-day variability—but also the unobserved heterogeneity within these variability types, thereby allowing the modeling of the underlying distributions of individual travel delays across different days with varying roadway disruption levels in a more effective and systematic way.     

Assessing social equity in distance based transit fares using a model of travel behavior

The goal of this study is to develop and apply a new method for assessing social equity impacts of distance-based public transit fares. Shifting to a distance-based fare structure can disproportionately favor or penalize different subgroups of a population based on variations in settlement patterns, travel needs, and most importantly, transit use. According to federal law, such disparities must be evaluated by the transit agency, but the area-based techniques identified by the Federal Transit Authority for assessing discrimination fail to account for disparities in distances travelled by transit users. This means that transit agencies currently lack guidelines for assessing the social equity impacts of replacing flat fare with distance-based fare structures. Our solution is to incorporate a joint ordinal/continuous model of trip generation and distance travelled into a GIS Decision Support System. The system enables a transit planner to visualize and compare distance travelled and transit-cost maps for different population profiles and fare structures. We apply the method to a case study in the Wasatch Front, Utah, where the Utah Transit Authority is exploring a switch to a distance-based fare structure. The analysis reveals that overall distance-based fares benefit low-income, elderly, and non-white populations. However, the effect is geographically uneven, and may be negative for members of these groups living on the urban fringe.     

Macroscopic Fundamental Diagrams: A cross-comparison of estimation methods

This paper aims to cross-compare existing estimation methods for the Macroscopic Fundamental Diagram. Raw data are provided by a mesoscopic simulation tool for two typical networks that mimic an urban corridor and a meshed urban center. We mainly focus on homogenous network loading in order to fairly cross-compare the different methods with the analytical reference. It appears that the only way to estimate the MFD without bias is to have the full information of vehicle trajectories over the network and to apply Edie’s definitions. Combining information from probes (mean network speed) and loop detectors (mean network flow) also provides accurate results even for low sampling rate (<10%). Loop detectors fail to provide a good estimation for mean network speed or density because they cannot capture the traffic spatial dynamics over links. This paper proposes a simple adjustment technic in order to reduce the discrepancy when only loop detectors are available.