Yandex updates Maps with walkable routes

Yandex has announced a new update for Yandex Maps that expands how walking routes are suggested. Instead of focusing mainly on the fastest way to reach a destination, the service can now recommend routes that match different situations, preferences, and accessibility needs.

The company said that walking is often influenced by context. A route that works well on a busy weekday may not be the preferred choice during a weekend stroll or while exploring a new city.

New Route Characteristics

According to Yandex, walking routes in Yandex Maps can now include clear characteristics that help users choose a path that fits their needs.

Examples include:

  • Leisurely
  • Lively
  • Easier
  • With ramps
  • Fewer stairs
  • No stairs
  • Optimal
  • Fast

The company noted that alternative routes previously often appeared as variations of the same path with different travel times. The new system is designed to provide routes that reflect different walking experiences rather than simply longer or shorter versions of the same route.

Routes Designed For Different Moments

Yandex shared several examples of how the new route options can be used.

For weekend walks, the Maps app may suggest a Leisurely route that passes through parks, waterfront areas, and local landmarks where possible.

For people exploring an unfamiliar neighborhood after dark, a Lively route may prioritize busier streets and avoid quieter sections when possible. The goal is to provide route suggestions that better align with the situation and the user’s preferences.

The company said the best route is not always the fastest one. Instead, the most suitable route can change depending on the time, location, and purpose of the walk.

AI And Mapping Data Behind The System

Yandex said that artificial intelligence plays a major role in generating these route recommendations.

Behind the scenes, AI analyzes a wide range of signals related to the urban environment. These include parks, public spaces, street types, elevation changes, stairs, and local activity patterns.

The company explained that one of the biggest challenges was teaching the system to understand concepts that people naturally recognize but are difficult for machines to define.

Yandex highlighted examples such as determining what makes a route feel “pleasant” or “lively.” Because there is no simple rule for these qualities, the company combined large language models (LLMs), machine learning, and mapping data to help train the system.

Focus On Real-World Preferences

According to Yandex, the result is navigation that better reflects how people experience routes in the real world rather than focusing only on travel time.

The company said the update is intended to help users choose paths that match their current needs, whether they are looking for a scenic walk, a more active area, or an accessible route with fewer obstacles.

Yandex summarized the idea behind the feature with a simple message: the fastest route is not always the best route, and sometimes the best route is the one that fits the moment.

Sources

  • Yandex (@yandexcom) posts on X, May 29, 2026.
Dan Taylor is the founder and editor of International Search Insights (ISI), the publication behind ReadIsi, and a Partner and Head of Innovation (Search & AI) at SALT.agency. He has worked in search for more than a decade, in-house and agency-side, and is regarded as one of the West’s leading voices on search engines beyond Google, including Yandex, Baidu, Naver and Cốc Cốc. On ReadIsi, Dan covers the engines and AI answer platforms that English-language coverage tends to miss: Baidu and ERNIE, Yandex and Alice AI, Naver, Cốc Cốc, Shenma, Sogou, WeChat and Weibo search, plus DeepSeek and Alibaba’s Qwen. He also keeps dated records of documented changes, such as the Yandex, Baidu and Naver algorithm update histories and the DeepSeek, ERNIE and Qwen model update histories. His approach is to take learnings from global search engines and apply logic with a high evidence threshold. A claim needs a source, patterns seen in one market are treated as a hypothesis until they are checked in another, and where the evidence is thin he says so. For brands, that means advice built on what can be shown, with measurement that separates being cited from being chosen. Dan is credited with coining the term “edge SEO” and won the inaugural TechSEO Boost research competition in 2018 for his work on CDN and edge changes for SEO. In 2026 he was credited with coining “Infinite Tail” to describe how keyword research is changing with AI search. He is the author of AI Search Marketing For The Travel Sector, writes for Search Engine Journal, Search Engine Land and Travel Marketing Guy, and has advised technology, SaaS, travel and enterprise ecommerce brands, including Cloudflare, GitLab and Airbnb. He has given multiple talks and panels at the International Search Summit in Barcelona, and has also spoken at Optimization Moscow, brightonSEO, Google Search Central Live Deep Dive Asia in Bangkok, Athens SEO and the Asia SEO Virtual Summit 2026, where his talk looked at how Baidu, Naver, DeepSeek, Tencent, Cốc Cốc and Yandex built their own AI answers.