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Vehicles estimation can be used in evaluating traffic conditions and facilitating traffic control, which is an important task in intelligent transportation system. The paper aims to propose a vehicle-counting method based on the analysis of surveillance videos.

The paper proposes a novel two-step method using low-rank representation (LRR) detection and locality-constrained linear coding (LLC) classification to count the number of vehicles in traffic video sequences automatically. The proposed method is based on an offline training to understand an LLC-based classifier with extracted features for vehicle and pedestrian classification, followed by an online counting algorithm to count the number of vehicles detected from the image sequence.

The proposed method allows delivery estimation (counting the number of vehicles at each frame only) and total number estimation of vehicles shown in the scene. The paper compares the proposed method with other similar methods on three public data sets. The experimental results show that the proposed method is competitive and effective in terms of computational speed and evaluation accuracy.

The proposed method does not consider illumination. Hence, the results might be unsatisfactory under low-lighting condition. Therefore, researchers are encouraged to add a term that controls the illumination changes into the energy function of vehicle detection in future work.

The paper bridges the gap between LRR detection and vehicle counting by taking advantage of existing LLC classification algorithm to distinguish different moving objects.

Across Europe, on average more than 95% of all passenger cars and half of all light commercial vehicles are permanently available to a household. This includes both privately owned vehicles and company cars. The profiles of vehicle use can be specified as average annual distance driven per vehicle and for the fleet as a total, purpose of travel (trip destination), infrastructure use (urban, interurban or motorway road transport) and also fuel consumption together with data on CO₂ emissions. Indicators on vehicle use can be tracked in various ways:

• self-administered panels of households, which permit their vehicles to be followed for several years;

• national or local household travel surveys (with a seven-day trip diary);

• official vehicle inspection and vehicle registration files;

• ‘vehicle surveys’ based on vehicle registry data;

• traffic counts;

• data collected for road-charging purposes.

self-administered panels of households, which permit their vehicles to be followed for several years;

national or local household travel surveys (with a seven-day trip diary);

official vehicle inspection and vehicle registration files;

‘vehicle surveys’ based on vehicle registry data;

traffic counts;

data collected for road-charging purposes.

The paper will present a review of mainly vehicle-based survey methods used in France, Germany, Finland, the United Kingdom, the United States and Canada, describing existing sampling frames to their scope, advantages and limitations, as well as their costs. Issues addressed in this context will be further examined in terms of their methodological challenges as well as their purpose.

The leading questions underlying this paper as well as the corresponding workshop are: why is it necessary to have data on passenger travel or transportation; and, looking at international experience, how good are vehicle-based surveys in delivering the required information? In discussing problems experienced in the different countries with data collection and evaluation methods, emphasis will be put on potential strategies for methodological and technological improvement and problem solving. One example is the potential use, benefits and constraints of new survey technologies presented by vehicle tracking techniques.

This paper provides an extensive review of surveys and data-collection programmes focused on urban goods movement (UGM). Surveys investigating passenger urban travel have a decades-long tradition. The same is not true for UGM. The first specific UGM surveys appeared about 10 years ago in response to the rapid growth of car traffic, congestion, pollution and lack of space. Most of the time, these surveys have been carried out to resolve specific, local problems concerning traffic. Only a few of them have taken a global approach to urban logistics by including all logistics operators (own-account and carriers), all delivery vehicles (heavy and light vehicles), all deliveries and pickups (from express to full payload) and an entire metropolitan area and surroundings. Due to various European programmes, an inventory has been created to analyse urban goods data collection according to spatial level and methodology of capture. With this inventory, European urban freight indicators can be described, along with the units in which they are measured and their purposes. The relevance of urban goods transport surveys lies in their capacity to give decision-makers an account of urban freight transport functioning, ratios and data, so as to help in formulating planning, regulation and forecasting. It appears that focusing on the movement (delivery/pick-up), as the unit of analysis in establishment-driver surveys is the most efficient approach to describe the generation of vehicular flow in the city. This fact is revealed in the French UGM surveys, which take into account the complexity of urban logistics.

Intelligent transportation systems (ITS) are defined as new infrastructures that combine people, roads and vehicles over the basis of modern embedded systems with enhanced digital connectivity. ITS is fast becoming a reality, favored in their development by the use of the internet. The purpose of this paper is to analyze the feasibility of road‐traffic management systems, using the internet as the communication link.

A literature review is presented to give a background in the progressive role that ITS and road safety and control applications play in society. The combination of internet and the ITS architecture is covered, and an electronic prototype providing web services for road‐traffic management is designed as an example. The scope of road traffic security research is extensive, and the use of artificial vision systems in road‐traffic analysis (technology which is on the basis of the developed prototype) is also discussed. The hardware and software characteristics of the prototype are defined.

The paper provides new insights into the use of the internet for road‐traffic management applications.

The research is limited to one particular road‐traffic management system. Results could be extended if more real equipments were analyzed. Also, end‐users' perception and their acceptance of the new technology could be examined using an information system theory like the technology acceptance model.

The paper shows the utility of the internet in the development of novel ITS.

This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine learning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic by predicting future traffic flow based on past flow and using those predictions to both maximize present traffic flow and decrease future traffic congestion.

ELMOPP was tested against the ITLC and OAF traffic management algorithms using a simulation modeled after the one presented in the ITLC paper, a single-intersection simulation.

The collected data supports the conclusion that ELMOPP statistically significantly outperforms both algorithms in throughput rate, a measure of how many vehicles are able to exit inroads every second.

Furthermore, while ITLC and OAF require the use of GPS transponders and GPS, speed sensors and radio, respectively, ELMOPP only uses traffic light camera footage, something that is almost always readily available in contrast to GPS and speed sensors.

The eight‐lane Santa Monica freeway winds languidly through southern California's urban megasprawl, carrying 330,000 cars, trucks, buses, vans and motorcycles each day through downtown Los Angeles, then east to the San Bernardino, Golden State and Santa Ana freeways, or west of the Harbor and Pasadena freeways.